Visualization in Bayesian workflow

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation...

Full description

Saved in:
Bibliographic Details
Published inJournal of the Royal Statistical Society. Series A, Statistics in society Vol. 182; no. 2; pp. 389 - 402
Main Authors Gabry, Jonah, Simpson, Daniel, Vehtari, Aki, Betancourt, Michael, Gelman, Andrew
Format Journal Article
LanguageEnglish
Published Oxford Wiley 01.02.2019
Oxford University Press
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.
AbstractList Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.
Author Gabry, Jonah
Betancourt, Michael
Gelman, Andrew
Simpson, Daniel
Vehtari, Aki
Author_xml – sequence: 1
  givenname: Jonah
  surname: Gabry
  fullname: Gabry, Jonah
– sequence: 2
  givenname: Daniel
  surname: Simpson
  fullname: Simpson, Daniel
– sequence: 3
  givenname: Aki
  surname: Vehtari
  fullname: Vehtari, Aki
– sequence: 4
  givenname: Michael
  surname: Betancourt
  fullname: Betancourt, Michael
– sequence: 5
  givenname: Andrew
  surname: Gelman
  fullname: Gelman, Andrew
BookMark eNp9kM1LwzAYh4NMcJtevAtFb0JnPpvmOIdfMBCcireQpgmk1mYmHWP-9XarehDxvfwuz_O-L78RGDS-MQAcIzhB3VyEGNUEYcLzPTBENOOpyNnLAAyhyGiKhMgPwCjGCm6H8yE4fXZxpWr3oVrnm8Q1yaXamOhUk6x9eLW1Xx-CfavqaI6-cgyerq8eZ7fp_P7mbjadp5pinKeIZNgqyoSBheCWaYVzQQzUWFtDqOnC8rJEFipecM2LsrQGm6KEkEFMSzIGZ_3eZfDvKxNbWflVaLqTEqOMIw4J4x0Fe0oHH2MwVmrX7p5vg3K1RFBum5DbJuSuiU45_6Usg3tTYfM3jHp47Wqz-YeUD4vF9Ns56Z0qtj78ODRnLKMYkk8Um3qr
CitedBy_id crossref_primary_10_1177_0093854819862010
crossref_primary_10_1016_j_applanim_2022_105747
crossref_primary_10_1126_sciadv_adk3222
crossref_primary_10_1371_journal_pone_0291153
crossref_primary_10_3389_fmicb_2021_638231
crossref_primary_10_1007_s10329_024_01172_2
crossref_primary_10_1111_rssa_12872
crossref_primary_10_3354_meps12946
crossref_primary_10_1016_j_eja_2024_127227
crossref_primary_10_3758_s13428_023_02204_3
crossref_primary_10_1016_j_socnet_2019_10_001
crossref_primary_10_1073_pnas_2112521118
crossref_primary_10_1016_j_chb_2021_106948
crossref_primary_10_1093_qopen_qoac015
crossref_primary_10_1016_j_foodres_2022_111565
crossref_primary_10_1111_add_14900
crossref_primary_10_1002_wics_1523
crossref_primary_10_3758_s13428_022_02020_1
crossref_primary_10_1007_s42978_020_00087_w
crossref_primary_10_1038_s41467_024_53608_4
crossref_primary_10_1515_lingty_2021_0002
crossref_primary_10_1186_s13063_020_04602_w
crossref_primary_10_1016_j_msksp_2025_103307
crossref_primary_10_1111_aec_13572
crossref_primary_10_1016_j_anbehav_2022_10_006
crossref_primary_10_21105_joss_03395
crossref_primary_10_1098_rspb_2023_1910
crossref_primary_10_1002_psp4_12455
crossref_primary_10_1016_j_spasta_2021_100544
crossref_primary_10_1016_j_evolhumbehav_2024_04_001
crossref_primary_10_1007_s10940_019_09430_z
crossref_primary_10_1017_S0142716423000267
crossref_primary_10_1038_s41586_020_2395_5
crossref_primary_10_1111_1365_2664_13883
crossref_primary_10_1016_j_neuroimage_2022_119195
crossref_primary_10_1097_PR9_0000000000000963
crossref_primary_10_1111_ele_14389
crossref_primary_10_3390_w15132353
crossref_primary_10_3758_s13415_021_00960_3
crossref_primary_10_7717_peerj_9383
crossref_primary_10_1016_j_fishres_2022_106250
crossref_primary_10_1017_ehs_2024_43
crossref_primary_10_1093_icesjms_fsz059
crossref_primary_10_1016_j_epidem_2019_100367
crossref_primary_10_3758_s13421_022_01288_0
crossref_primary_10_1016_j_cortex_2022_05_015
crossref_primary_10_1111_faf_12878
crossref_primary_10_1371_journal_pone_0225872
crossref_primary_10_1111_vox_13639
crossref_primary_10_1016_j_bandc_2021_105811
crossref_primary_10_3390_en13123183
crossref_primary_10_3390_plants11060801
crossref_primary_10_1080_21678421_2021_1908362
crossref_primary_10_3389_fpsyg_2020_611963
crossref_primary_10_1016_j_jocd_2020_05_003
crossref_primary_10_1111_1365_2656_70007
crossref_primary_10_1111_1365_2656_13532
crossref_primary_10_3758_s13415_022_01047_3
crossref_primary_10_1111_1365_2656_13779
crossref_primary_10_5334_labphon_213
crossref_primary_10_1111_jbi_14760
crossref_primary_10_1126_sciadv_abn3999
crossref_primary_10_1097_JSM_0000000000001102
crossref_primary_10_1177_14738716241265120
crossref_primary_10_1186_s40359_025_02517_2
crossref_primary_10_1016_j_dcn_2024_101459
crossref_primary_10_1111_gcb_15862
crossref_primary_10_1080_24733938_2021_2013522
crossref_primary_10_3389_fvets_2022_937904
crossref_primary_10_1088_1748_9326_acf8db
crossref_primary_10_1088_1361_6560_ab3a5a
crossref_primary_10_1016_j_jmp_2020_102474
crossref_primary_10_1111_ele_14008
crossref_primary_10_1002_dev_22226
crossref_primary_10_1002_jnr_24771
crossref_primary_10_3389_fspor_2022_1042494
crossref_primary_10_1111_mec_16081
crossref_primary_10_1136_bmjopen_2024_085406
crossref_primary_10_1016_j_neuropsychologia_2021_107834
crossref_primary_10_1029_2020GL087972
crossref_primary_10_1027_1864_9335_a000542
crossref_primary_10_1098_rsos_201092
crossref_primary_10_1016_j_ijforecast_2025_02_005
crossref_primary_10_1007_s00148_024_01043_6
crossref_primary_10_7717_peerj_16117
crossref_primary_10_1111_oik_08393
crossref_primary_10_7554_eLife_77632
crossref_primary_10_1126_science_adh8830
crossref_primary_10_1016_j_yhbeh_2024_105604
crossref_primary_10_1214_20_AOAS1372
crossref_primary_10_1002_esp_5192
crossref_primary_10_1007_s12187_024_10143_4
crossref_primary_10_1080_24733938_2020_1862419
crossref_primary_10_1111_jzo_12846
crossref_primary_10_1111_oik_08278
crossref_primary_10_3389_fevo_2022_910121
crossref_primary_10_1142_S0219525923400015
crossref_primary_10_1038_s41559_024_02381_0
crossref_primary_10_1038_s41559_023_02298_0
crossref_primary_10_1002_ece3_9901
crossref_primary_10_1080_01621459_2021_1938081
crossref_primary_10_1016_j_ecoenv_2023_115406
crossref_primary_10_1371_journal_pcbi_1010082
crossref_primary_10_1080_08982112_2023_2276779
crossref_primary_10_1002_pst_2266
crossref_primary_10_1080_10409289_2020_1749492
crossref_primary_10_12688_openreseurope_17749_1
crossref_primary_10_1177_87552930211067817
crossref_primary_10_1007_s11222_023_10295_3
crossref_primary_10_3354_dao03812
crossref_primary_10_1111_btp_12981
crossref_primary_10_1038_s41562_024_01988_4
crossref_primary_10_1214_23_BA1381
crossref_primary_10_1093_jnci_djab186
crossref_primary_10_1021_acs_est_0c06268
crossref_primary_10_3390_plants12173040
crossref_primary_10_1111_oik_10495
crossref_primary_10_1109_TVCG_2021_3073466
crossref_primary_10_3389_fmars_2024_1452984
crossref_primary_10_1016_j_foreco_2023_121305
crossref_primary_10_1002_aqc_70013
crossref_primary_10_1128_aem_01419_23
crossref_primary_10_1111_1365_2656_13618
crossref_primary_10_1080_10888438_2019_1642341
crossref_primary_10_1111_1365_2435_14287
crossref_primary_10_1371_journal_pone_0233282
crossref_primary_10_1016_j_therwi_2022_100010
crossref_primary_10_1111_acv_12915
crossref_primary_10_1061__ASCE_CO_1943_7862_0001930
crossref_primary_10_1177_1477370821997323
crossref_primary_10_2118_209203_PA
crossref_primary_10_1002_ece3_8030
crossref_primary_10_1111_nph_20358
crossref_primary_10_7759_cureus_59482
crossref_primary_10_1038_s41467_022_33570_9
crossref_primary_10_1111_nph_18150
crossref_primary_10_1371_journal_pone_0253043
crossref_primary_10_2139_ssrn_4350792
crossref_primary_10_1111_1365_2745_14154
crossref_primary_10_3847_1538_4357_abc41a
crossref_primary_10_1088_1748_9326_ad0c88
crossref_primary_10_1177_19367244231172144
crossref_primary_10_1515_hsz_2023_0205
crossref_primary_10_1002_wsb_1504
crossref_primary_10_1038_s41586_024_07980_2
crossref_primary_10_1094_PHYTO_10_21_0430_PER
crossref_primary_10_1214_21_AOAS1587
crossref_primary_10_1002_ajp_23674
crossref_primary_10_1214_21_AOAS1580
crossref_primary_10_1021_acs_jproteome_3c00297
crossref_primary_10_7554_eLife_62714
crossref_primary_10_7717_peerj_9436
crossref_primary_10_1371_journal_pcbi_1011575
crossref_primary_10_1016_j_scitotenv_2023_164223
crossref_primary_10_1111_1365_2435_14340
crossref_primary_10_1186_s12879_021_06757_6
crossref_primary_10_1007_s10531_023_02713_9
crossref_primary_10_1016_j_gecco_2022_e02109
crossref_primary_10_1111_jzo_12877
crossref_primary_10_1007_s00180_022_01231_6
crossref_primary_10_1007_s40003_020_00465_4
crossref_primary_10_3389_fcomp_2020_567344
crossref_primary_10_1126_science_abf2343
crossref_primary_10_1038_s41559_022_01893_x
crossref_primary_10_3389_fpsyg_2021_672927
crossref_primary_10_1002_jcc_26556
crossref_primary_10_1002_pbc_29036
crossref_primary_10_1111_2041_210X_13974
crossref_primary_10_1145_3490953
crossref_primary_10_1016_j_fishres_2024_107024
crossref_primary_10_1016_j_specom_2024_103121
crossref_primary_10_3389_fpsyg_2024_1281857
crossref_primary_10_1177_03611981221125215
crossref_primary_10_1016_j_fishres_2021_106002
crossref_primary_10_1214_22_AOAS1711
crossref_primary_10_1242_jeb_229609
crossref_primary_10_3847_1538_4357_ab9607
crossref_primary_10_1002_smj_3665
crossref_primary_10_1111_ejn_14611
crossref_primary_10_1111_jeb_14222
crossref_primary_10_1080_00273171_2020_1805582
crossref_primary_10_1371_journal_pbio_3001389
crossref_primary_10_3758_s13423_019_01637_2
crossref_primary_10_1016_j_quageo_2024_101502
crossref_primary_10_1016_j_wocn_2021_101116
crossref_primary_10_3389_fgene_2021_657375
crossref_primary_10_1002_ajb2_16076
crossref_primary_10_1111_ddi_13680
crossref_primary_10_1002_ail2_22
crossref_primary_10_1093_g3journal_jkae013
crossref_primary_10_1093_mnras_staf238
crossref_primary_10_1007_s40273_024_01387_7
crossref_primary_10_1016_j_neuroimage_2023_120393
crossref_primary_10_1098_rspb_2024_2941
crossref_primary_10_1521_jscp_2025_44_1_003
crossref_primary_10_21105_joss_04716
crossref_primary_10_1016_j_trf_2020_11_002
crossref_primary_10_1214_21_STS839
crossref_primary_10_1111_fog_12707
crossref_primary_10_3389_fneur_2021_804139
crossref_primary_10_1007_s10584_024_03763_w
crossref_primary_10_1086_726785
crossref_primary_10_1002_asmb_2832
crossref_primary_10_1007_s11104_022_05329_0
crossref_primary_10_1002_ecs2_4798
crossref_primary_10_1111_gcb_15650
crossref_primary_10_1111_1365_2664_14764
crossref_primary_10_1002_nafm_10724
crossref_primary_10_1145_3476980
crossref_primary_10_1186_s13690_022_01002_1
crossref_primary_10_1186_s42408_023_00183_6
crossref_primary_10_1007_s10816_023_09611_y
crossref_primary_10_1007_s42113_023_00168_3
crossref_primary_10_1088_1361_6471_abc3a5
crossref_primary_10_1051_0004_6361_201937347
crossref_primary_10_1109_TVCG_2022_3226463
crossref_primary_10_1123_ijspp_2019_0534
crossref_primary_10_1016_j_addbeh_2022_107330
crossref_primary_10_1016_j_jglr_2024_102424
crossref_primary_10_1051_0004_6361_202039461
crossref_primary_10_1007_s10584_021_03022_2
crossref_primary_10_6339_23_JDS1113
crossref_primary_10_1111_2041_210X_13407
crossref_primary_10_7717_peerj_9511
crossref_primary_10_2196_45321
crossref_primary_10_1038_s41893_024_01494_5
crossref_primary_10_1038_s41598_021_03405_6
crossref_primary_10_1002_2688_8319_12116
crossref_primary_10_3390_psych5020027
crossref_primary_10_1002_ecs2_4543
crossref_primary_10_1017_aae_2021_25
crossref_primary_10_1002_nafm_10739
crossref_primary_10_1016_j_jmp_2022_102695
crossref_primary_10_1002_joc_8793
crossref_primary_10_1016_j_jfp_2023_100138
crossref_primary_10_1162_nol_a_00094
crossref_primary_10_1093_jssam_smaa008
crossref_primary_10_1016_j_foreco_2023_120969
crossref_primary_10_1371_journal_pcbi_1012777
crossref_primary_10_1002_ajpa_25048
crossref_primary_10_1038_s41598_024_72812_2
crossref_primary_10_1080_02640414_2025_2482367
crossref_primary_10_1111_2041_210X_13774
crossref_primary_10_3847_1538_4357_adb8df
crossref_primary_10_1016_j_conctc_2021_100709
crossref_primary_10_1002_aps3_11503
crossref_primary_10_1002_pan3_10502
crossref_primary_10_1038_s41598_022_14662_4
crossref_primary_10_1051_0004_6361_202245191
crossref_primary_10_1063_5_0154773
crossref_primary_10_1214_23_SS145
crossref_primary_10_1098_rsif_2024_0255
crossref_primary_10_1080_10705511_2021_1915146
crossref_primary_10_1111_plb_13469
crossref_primary_10_1371_journal_pcbi_1008039
crossref_primary_10_3389_fmars_2021_719956
crossref_primary_10_1371_journal_pcbi_1009001
crossref_primary_10_1002_jclp_23139
crossref_primary_10_1038_s41559_024_02533_2
crossref_primary_10_1002_ecy_3475
crossref_primary_10_3310_hsdr09130
crossref_primary_10_1016_j_jfp_2023_100135
crossref_primary_10_3847_1538_4357_ad53c7
crossref_primary_10_1016_j_ecoinf_2023_102271
crossref_primary_10_1016_j_anbehav_2024_09_009
crossref_primary_10_1186_s41235_022_00387_5
crossref_primary_10_1643_i2024024
crossref_primary_10_1080_00336297_2024_2364608
crossref_primary_10_1371_journal_pone_0252227
crossref_primary_10_1002_eap_70016
crossref_primary_10_1093_bioinformatics_btac134
crossref_primary_10_1111_mec_17532
crossref_primary_10_1016_j_neuroimage_2020_116922
crossref_primary_10_1093_jeb_voaf010
crossref_primary_10_1098_rspb_2023_2101
crossref_primary_10_1098_rstb_2019_0732
crossref_primary_10_1111_bmsp_12302
crossref_primary_10_1007_s11336_023_09924_7
crossref_primary_10_1038_s41598_024_57426_y
crossref_primary_10_1093_jac_dkab255
crossref_primary_10_1109_TVCG_2021_3114824
crossref_primary_10_2196_50023
crossref_primary_10_1016_j_anbehav_2024_09_001
crossref_primary_10_1038_s43586_020_00001_2
crossref_primary_10_1002_ecm_1557
crossref_primary_10_1002_ecs2_3546
crossref_primary_10_1080_02640414_2023_2203484
crossref_primary_10_1111_cdev_13950
crossref_primary_10_1007_s42113_019_00051_0
crossref_primary_10_1109_TSE_2019_2935974
crossref_primary_10_1029_2021GB007160
crossref_primary_10_1111_eth_13129
crossref_primary_10_1007_s10764_022_00342_7
crossref_primary_10_1111_jiec_13339
crossref_primary_10_1111_mec_16436
crossref_primary_10_1016_j_oneear_2023_12_001
crossref_primary_10_1016_j_ecoinf_2023_102375
crossref_primary_10_1111_rec_13652
crossref_primary_10_1016_j_chest_2024_08_055
crossref_primary_10_1016_j_earlhumdev_2020_105272
crossref_primary_10_3758_s13428_024_02370_y
crossref_primary_10_1111_1365_2435_14743
crossref_primary_10_1038_s41386_020_0725_9
crossref_primary_10_1080_00401706_2023_2190770
crossref_primary_10_1186_s12874_022_01813_4
crossref_primary_10_3390_axioms10040276
crossref_primary_10_7554_eLife_82996
crossref_primary_10_1109_TVCG_2024_3456402
crossref_primary_10_1007_s10826_023_02610_3
crossref_primary_10_1017_S1366728924000828
crossref_primary_10_1371_journal_pone_0276336
crossref_primary_10_1017_S1355617722000054
crossref_primary_10_17531_ein_2022_2_6
crossref_primary_10_1002_ece3_70201
crossref_primary_10_1080_03610918_2022_2025841
crossref_primary_10_1080_10705511_2021_1971527
crossref_primary_10_1093_ofid_ofab235
crossref_primary_10_1016_j_biocon_2023_110085
crossref_primary_10_1038_s41558_024_02093_0
crossref_primary_10_1111_emr_70001
crossref_primary_10_1111_sms_13701
crossref_primary_10_18332_tid_183804
crossref_primary_10_1002_evl3_245
crossref_primary_10_1002_env_2894
crossref_primary_10_1146_annurev_statistics_033121_110134
crossref_primary_10_1186_s12889_024_19889_6
crossref_primary_10_1002_sim_9164
crossref_primary_10_1038_s41598_024_73612_4
crossref_primary_10_1016_j_ecolecon_2020_106712
crossref_primary_10_1007_s10980_024_01912_1
crossref_primary_10_1017_dce_2021_18
crossref_primary_10_1177_03611981221119189
crossref_primary_10_3389_fgene_2023_1143395
crossref_primary_10_3390_d14100858
crossref_primary_10_1186_s12874_024_02333_z
crossref_primary_10_1097_AJP_0000000000000987
crossref_primary_10_3354_meps13447
crossref_primary_10_1186_s13741_023_00324_0
crossref_primary_10_1371_journal_pone_0231982
crossref_primary_10_1002_ecy_3677
crossref_primary_10_1089_neur_2022_0092
crossref_primary_10_1214_23_STS907
crossref_primary_10_1177_14761270211072248
crossref_primary_10_1155_2024_9719635
crossref_primary_10_1111_lnc3_12439
crossref_primary_10_1177_01427237231169398
crossref_primary_10_1007_s10764_024_00468_w
crossref_primary_10_1002_ecs2_3739
crossref_primary_10_1002_jwmg_22725
crossref_primary_10_3390_mca25030041
crossref_primary_10_3758_s13423_021_01986_x
crossref_primary_10_1080_24694452_2020_1756207
crossref_primary_10_5194_hess_28_4685_2024
crossref_primary_10_1007_s10641_022_01296_8
crossref_primary_10_1186_s12874_023_01963_z
crossref_primary_10_1007_s00431_020_03818_x
crossref_primary_10_1093_jrsssc_qlae044
crossref_primary_10_1016_j_fcr_2022_108477
crossref_primary_10_1080_02664763_2024_2338404
crossref_primary_10_2139_ssrn_4993829
crossref_primary_10_1093_cid_ciab678
crossref_primary_10_1515_lingty_2023_0076
crossref_primary_10_1111_geb_13591
crossref_primary_10_1093_jrsssa_qnae023
crossref_primary_10_1038_s41541_024_00951_8
crossref_primary_10_1038_s41598_022_06875_4
crossref_primary_10_1080_10705511_2022_2046475
crossref_primary_10_1214_22_AOAS1657
crossref_primary_10_1016_j_foreco_2024_122149
crossref_primary_10_3354_meps14382
crossref_primary_10_3390_insects16010064
crossref_primary_10_1214_23_AOAS1791
crossref_primary_10_1002_psp4_12812
crossref_primary_10_1111_jzo_13012
crossref_primary_10_3390_asi2030024
crossref_primary_10_3390_jfmk6020036
crossref_primary_10_3233_SJI_220965
crossref_primary_10_7554_eLife_84602
crossref_primary_10_1016_j_neuropsychologia_2021_108120
crossref_primary_10_1016_j_simpa_2020_100016
crossref_primary_10_1016_j_ecoinf_2024_102865
crossref_primary_10_1002_bimj_202200095
crossref_primary_10_1016_j_apergo_2022_103739
crossref_primary_10_1002_mcf2_10225
crossref_primary_10_1016_j_jen_2020_11_008
crossref_primary_10_1177_00220426221098986
crossref_primary_10_1111_1365_2664_14497
crossref_primary_10_1111_1365_2664_14014
crossref_primary_10_1111_fme_12638
crossref_primary_10_1016_j_fishres_2025_107307
crossref_primary_10_1016_j_strusafe_2024_102503
crossref_primary_10_1021_acs_chemmater_3c02751
crossref_primary_10_1016_j_agee_2023_108765
crossref_primary_10_1021_acs_est_4c09395
crossref_primary_10_1098_rsos_210274
crossref_primary_10_1016_j_anbehav_2023_06_014
crossref_primary_10_7717_peerj_14726
crossref_primary_10_1080_15305058_2023_2214647
crossref_primary_10_1071_MF23088
crossref_primary_10_1093_sysbio_syae044
crossref_primary_10_1093_jrsssc_qlae067
crossref_primary_10_5194_nhess_21_1599_2021
crossref_primary_10_1029_2020WR027897
crossref_primary_10_1007_s10530_021_02712_3
crossref_primary_10_3354_meps13894
crossref_primary_10_1016_j_cub_2025_02_016
crossref_primary_10_3390_ani10112009
crossref_primary_10_1098_rspb_2023_2480
crossref_primary_10_1126_science_add8606
crossref_primary_10_1146_annurev_astro_052920_103508
crossref_primary_10_1007_s00442_021_04853_7
crossref_primary_10_3390_v16081315
crossref_primary_10_1038_s41467_020_20455_y
crossref_primary_10_1016_j_neurobiolaging_2024_08_001
crossref_primary_10_1111_meca_12366
crossref_primary_10_1371_journal_pone_0310021
crossref_primary_10_1016_j_jenvrad_2022_107077
crossref_primary_10_1038_s41598_022_12312_3
crossref_primary_10_1038_s41598_023_36974_9
crossref_primary_10_1186_s13063_021_05759_8
crossref_primary_10_26508_lsa_202201855
crossref_primary_10_1590_rbce_45_e20240015
crossref_primary_10_1139_cjfas_2022_0024
crossref_primary_10_1177_17479541221116881
crossref_primary_10_1002_2688_8319_12293
crossref_primary_10_1007_s40865_022_00204_z
crossref_primary_10_1093_jxb_erae290
crossref_primary_10_1109_TVCG_2023_3326516
crossref_primary_10_1016_j_patter_2020_100079
crossref_primary_10_1007_s11129_024_09289_w
crossref_primary_10_1214_24_AOAS1934
crossref_primary_10_1007_s10661_022_10022_x
crossref_primary_10_1016_j_jss_2023_111909
crossref_primary_10_1039_D1JA00407G
crossref_primary_10_1111_1759_3441_12383
crossref_primary_10_3391_ai_2024_19_1_116040
crossref_primary_10_1002_cjs_11637
crossref_primary_10_1007_s13253_024_00639_5
crossref_primary_10_1007_s00265_021_03111_3
crossref_primary_10_1002_ece3_11531
crossref_primary_10_1038_s41562_021_01211_8
crossref_primary_10_1111_fog_12469
crossref_primary_10_1214_22_BA1327
crossref_primary_10_3390_app142210411
crossref_primary_10_1098_rstb_2021_0299
crossref_primary_10_1139_cjfas_2021_0224
crossref_primary_10_1017_S0954579423001335
crossref_primary_10_3390_urbansci8040176
crossref_primary_10_1080_02640414_2019_1612505
crossref_primary_10_1016_j_jcp_2023_112210
crossref_primary_10_1111_jfb_14181
crossref_primary_10_1007_s12662_024_00998_8
crossref_primary_10_1080_02705060_2022_2085199
crossref_primary_10_1038_s41559_021_01442_y
crossref_primary_10_1093_treephys_tpad136
crossref_primary_10_1016_j_rama_2024_04_001
crossref_primary_10_1073_pnas_2221961120
crossref_primary_10_1109_TCCN_2021_3066566
crossref_primary_10_1111_1365_2656_13834
crossref_primary_10_1093_toxsci_kfae159
crossref_primary_10_1109_TVCG_2020_3030335
crossref_primary_10_1002_hbm_26107
crossref_primary_10_1017_nps_2021_71
crossref_primary_10_24072_pcjournal_347
crossref_primary_10_1017_ice_2021_530
crossref_primary_10_1016_j_baae_2022_08_004
crossref_primary_10_3389_fvets_2022_1027883
crossref_primary_10_1126_science_abn7829
crossref_primary_10_2139_ssrn_4468022
crossref_primary_10_1016_j_spasta_2020_100450
crossref_primary_10_1016_j_neuroimage_2022_119149
crossref_primary_10_1027_1015_5759_a000624
crossref_primary_10_1371_journal_pone_0252527
crossref_primary_10_1002_wlb3_01340
crossref_primary_10_1038_s41598_023_48980_y
crossref_primary_10_1109_TVCG_2019_2934287
crossref_primary_10_3390_jintelligence13030026
crossref_primary_10_1016_j_anbehav_2023_05_003
crossref_primary_10_1155_2022_5636449
crossref_primary_10_1002_ecs2_70090
crossref_primary_10_1016_j_learninstruc_2025_102102
crossref_primary_10_1002_hbm_70052
crossref_primary_10_1016_j_jfoodeng_2021_110634
crossref_primary_10_1016_j_epidem_2023_100715
crossref_primary_10_1016_j_ecolind_2023_110564
crossref_primary_10_1371_journal_pone_0298736
crossref_primary_10_1016_j_tranpol_2021_01_013
crossref_primary_10_1002_sdr_1693
crossref_primary_10_1371_journal_pone_0265730
crossref_primary_10_1080_00949655_2024_2449534
crossref_primary_10_1016_j_neuroimage_2020_117708
crossref_primary_10_1002_mar_22095
crossref_primary_10_1016_j_crpvbd_2023_100125
crossref_primary_10_1007_s00227_022_04054_7
crossref_primary_10_1002_hbm_26490
crossref_primary_10_1038_s41598_024_68175_3
crossref_primary_10_3389_fnagi_2023_1225816
crossref_primary_10_1097_ALN_0000000000004510
crossref_primary_10_1109_JSEN_2023_3272907
crossref_primary_10_2196_24266
crossref_primary_10_3354_meps13286
crossref_primary_10_1016_j_jsr_2023_09_006
crossref_primary_10_1021_acs_jproteome_1c00859
crossref_primary_10_4236_ape_2021_111008
crossref_primary_10_1002_eap_2552
crossref_primary_10_1016_j_ocecoaman_2021_105947
crossref_primary_10_1155_2019_4784909
crossref_primary_10_1007_s10661_022_10024_9
crossref_primary_10_1038_s41550_019_0998_2
crossref_primary_10_1038_s41598_021_91437_3
crossref_primary_10_1038_s41586_023_06113_5
crossref_primary_10_1002_ecy_3804
crossref_primary_10_1214_20_EJS1711
crossref_primary_10_1080_02640414_2020_1766177
crossref_primary_10_1002_ar_25639
crossref_primary_10_1001_jamanetworkopen_2023_44399
crossref_primary_10_1007_s10914_022_09624_6
crossref_primary_10_1111_jbi_14599
crossref_primary_10_1186_s40658_023_00537_8
crossref_primary_10_1017_ice_2021_323
crossref_primary_10_1111_faf_12730
crossref_primary_10_1098_rsos_210537
crossref_primary_10_1186_s12889_021_10690_3
crossref_primary_10_3389_fvets_2020_00345
crossref_primary_10_1214_23_BA1404
crossref_primary_10_1016_j_marpolbul_2024_117487
crossref_primary_10_1186_s40249_023_01065_4
crossref_primary_10_1016_j_csda_2023_107795
crossref_primary_10_1002_ecm_1515
crossref_primary_10_1111_gcb_16067
crossref_primary_10_3390_stats6010011
crossref_primary_10_1186_s12874_024_02149_x
crossref_primary_10_3389_fpsyg_2021_742577
crossref_primary_10_1002_aur_2728
crossref_primary_10_1007_s00477_023_02615_w
crossref_primary_10_1073_pnas_2108731119
crossref_primary_10_1080_02626667_2022_2095207
crossref_primary_10_7717_peerj_17721
crossref_primary_10_1016_j_chemosphere_2025_144287
crossref_primary_10_1016_j_anbehav_2024_05_016
crossref_primary_10_1016_j_anbehav_2024_11_006
crossref_primary_10_1080_02640414_2024_2329846
crossref_primary_10_1126_sciadv_adr0278
crossref_primary_10_1214_24_STS949
crossref_primary_10_1515_jqas_2022_0021
crossref_primary_10_1002_ecs2_70083
crossref_primary_10_1007_s42113_023_00173_6
crossref_primary_10_1080_01446193_2024_2309890
crossref_primary_10_1093_cz_zoac016
crossref_primary_10_1111_ssqu_13190
crossref_primary_10_1007_s00265_024_03492_1
crossref_primary_10_1007_s41666_021_00094_8
crossref_primary_10_1017_ehs_2021_14
crossref_primary_10_2118_209234_PA
crossref_primary_10_1109_TVCG_2021_3114684
crossref_primary_10_24072_pcjournal_294
crossref_primary_10_1111_rssc_12595
crossref_primary_10_1029_2023WR036076
crossref_primary_10_1002_sim_9480
crossref_primary_10_1177_0956797619830649
crossref_primary_10_3847_1538_4357_acea7c
crossref_primary_10_1002_mpr_1868
crossref_primary_10_1002_edn3_312
crossref_primary_10_1098_rsos_201848
crossref_primary_10_1177_00315125221150476
crossref_primary_10_1182_bloodadvances_2021005710
crossref_primary_10_1177_00953997251317523
crossref_primary_10_1145_3656409
crossref_primary_10_1007_s11222_023_10325_0
Cites_doi 10.1201/b16018
10.1214/08-AOAS191
10.1016/S0140-6736(15)00128-2
10.1198/106186004X11435
10.1511/2014.111.460
10.1093/oso/9780198522669.003.0009
10.1214/16-STS576
10.3390/e19100555
10.1007/978-0-387-98141-3
10.1007/s11222-016-9696-4
ContentType Journal Article
Copyright 2019 Royal Statistical Society
Copyright © 2019 The Royal Statistical Society and John Wiley & Sons Ltd
Copyright_xml – notice: 2019 Royal Statistical Society
– notice: Copyright © 2019 The Royal Statistical Society and John Wiley & Sons Ltd
DBID AAYXX
CITATION
7SC
8BJ
8FD
FQK
JBE
JQ2
L7M
L~C
L~D
DOI 10.1111/rssa.12378
DatabaseName CrossRef
Computer and Information Systems Abstracts
International Bibliography of the Social Sciences (IBSS)
Technology Research Database
International Bibliography of the Social Sciences
International Bibliography of the Social Sciences
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
International Bibliography of the Social Sciences (IBSS)
Technology Research Database
Computer and Information Systems Abstracts – Academic
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts Professional
DatabaseTitleList CrossRef
International Bibliography of the Social Sciences (IBSS)


DeliveryMethod fulltext_linktorsrc
Discipline Statistics
Mathematics
EISSN 1467-985X
EndPage 402
ExternalDocumentID 10_1111_rssa_12378
RSSA12378
48556420
Genre article
GrantInformation_xml – fundername: Columbia University
– fundername: US National Science Foundation
– fundername: Institute for Education Sciences
– fundername: Sloan Foundation
– fundername: Defense Advanced Research Projects Agency
– fundername: Office of Naval Research
GroupedDBID -~X
.3N
.GA
05W
10A
1OC
29L
2AX
33P
3SF
4.4
50Y
50Z
51W
51X
52M
52N
52O
52P
52S
52T
52U
52W
52X
5GY
5HH
5LA
66C
7PT
8-0
8-1
8-3
8UM
8VB
930
A03
AAESR
AAEVG
AAONW
AASGY
AAUAY
AAXRX
AAZKR
ABBHK
ABCQN
ABCUV
ABDFA
ABEML
ABFAN
ABIVO
ABPFR
ABPQH
ABPTD
ABWST
ABXSQ
ABYWD
ACAHQ
ACCZN
ACGFS
ACIWK
ACMTB
ACNCT
ACPOU
ACSCC
ACTMH
ACXBN
ACXQS
ADBBV
ADEOM
ADIZJ
ADKYN
ADMGS
ADODI
ADOZA
ADRDM
ADVEK
ADZMN
AEGXH
AEIMD
AEMOZ
AEUPB
AFBPY
AFEBI
AFGKR
AFVYC
AFXHP
AFZJQ
AHQJS
AIURR
AJAOE
AJNCP
AJXKR
AKVCP
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALRMG
ALUQN
AMBMR
AMVHM
AMYDB
ATUGU
AUFTA
AZBYB
AZVAB
BAFTC
BCRHZ
BDRZF
BHBCM
BMNLL
BMXJE
BNHUX
BROTX
BRXPI
BY8
CAG
CJ0
CO8
CS3
D-E
DCZOG
DPXWK
DQDLB
DR2
DRFUL
DRSTM
DSRWC
EBA
EBO
EBR
EBS
EBU
ECEWR
EJD
EMK
EOH
F00
F5P
G-S
G.N
GODZA
H.T
H.X
HQ6
HZI
HZ~
IPSME
IX1
J0M
JAAYA
JAS
JBMMH
JBZCM
JENOY
JHFFW
JKQEH
JLEZI
JLXEF
JMS
JPL
JST
K1G
K48
LATKE
LC2
LC3
LEEKS
LH4
LITHE
LOXES
LP6
LP7
LUTES
LYRES
MK4
MRFUL
MRSTM
MSFUL
MSSTM
MXFUL
MXSTM
N04
N05
NF~
NU-
O66
O9-
OIG
P2W
P2X
P4D
PQQKQ
Q.N
Q11
QB0
QWB
R.K
ROL
ROX
RX1
SA0
SUPJJ
TH9
TN5
UB1
W8V
W99
WBKPD
WIH
WIK
WOHZO
WQJ
WYISQ
XBAML
XG1
YF5
YQT
ZL0
ZZTAW
~IA
~WT
.Y3
07C
1OB
1OL
3-9
31~
AAHHS
AANHP
AARHZ
ABYAD
ACBWZ
ACCFJ
ACFRR
ACRPL
ACTWD
ACUBG
ACYXJ
ADNMO
ADQBN
ADULT
AEEZP
AELPN
AEQDE
AEUQT
AFPWT
AIWBW
AJBDE
ANFBD
ASPBG
AS~
ATGXG
AVWKF
AZFZN
COF
FEDTE
FVMVE
H13
HF~
HGD
HVGLF
H~9
IHE
JSODD
LW6
MVM
RJQFR
RNS
VUG
ZGI
AAYXX
CITATION
7SC
8BJ
8FD
FQK
JBE
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c4228-1362fa459e0b97f5ca2893e0c2cfe34e2cff7dd1f0a7b7c7bddfe2ebd005024d3
IEDL.DBID DR2
ISSN 0964-1998
IngestDate Wed Aug 13 09:53:39 EDT 2025
Thu Apr 24 22:51:35 EDT 2025
Tue Jul 01 00:50:56 EDT 2025
Wed Jan 22 16:34:03 EST 2025
Thu Jul 03 21:54:23 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 2
Language English
License https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c4228-1362fa459e0b97f5ca2893e0c2cfe34e2cff7dd1f0a7b7c7bddfe2ebd005024d3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
PQID 2167170357
PQPubID 105636
PageCount 14
ParticipantIDs proquest_journals_2167170357
crossref_citationtrail_10_1111_rssa_12378
crossref_primary_10_1111_rssa_12378
wiley_primary_10_1111_rssa_12378_RSSA12378
jstor_primary_48556420
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate February 2019
PublicationDateYYYYMMDD 2019-02-01
PublicationDate_xml – month: 02
  year: 2019
  text: February 2019
PublicationDecade 2010
PublicationPlace Oxford
PublicationPlace_xml – name: Oxford
PublicationTitle Journal of the Royal Statistical Society. Series A, Statistics in society
PublicationYear 2019
Publisher Wiley
Oxford University Press
Publisher_xml – name: Wiley
– name: Oxford University Press
References 2017c
2017b
2017a
2017b; 27
2015; 386
2017; 32
2004; 13
2009
2018; 80
2017
2017; 19
1992
2013
2008; 2
2009; 367
2014; 102
2018; 68
R Core Team (2023030719372669700_) 2017
Gelman (2023030719372669700_) 2008; 2
Wickham (2023030719372669700_) 2009
Gelman (2023030719372669700_) 2017; 19
Gabry (2023030719372669700_) 2017
Stan Development Team (2023030719372669700_) 2017
Betancourt (2023030719372669700_) 2017
Gelman (2023030719372669700_) 2013
Shaddick (2023030719372669700_) 2018; 68
Gelman (2023030719372669700_) 2014; 102
Vehtari (2023030719372669700_) 2017
Simpson (2023030719372669700_) 2017; 32
Gelfand (2023030719372669700_) 1992
Vehtari (2023030719372669700_) 2017; 27
Forouzanfar (2023030719372669700_) 2015; 386
Yao (2023030719372669700_) 2018; 80
Buja (2023030719372669700_) 2009; 367
Gelman (2023030719372669700_) 2004; 13
References_xml – year: 2009
– volume: 68
  start-page: 231
  year: 2018
  end-page: 253
  article-title: Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution
  publication-title: Appl. Statist.
– start-page: 147
  year: 1992
  end-page: 167
– volume: 32
  start-page: 1
  year: 2017
  end-page: 28
  article-title: Penalising model component complexity: a principled, practical approach to constructing priors
  publication-title: Statist. Sci.
– year: 2017c
– volume: 2
  start-page: 1360
  year: 2008
  end-page: 1383
  article-title: A weakly informative default prior distribution for logistic and other regression models
  publication-title: Ann. Appl. Statist.
– year: 2017a
– volume: 80
  start-page: 5581
  year: 2018
  end-page: 5590
  article-title: Yes, but did it work?: Evaluating variational inference
  publication-title: Proc. Mach. Learn. Res.
– year: 2017b
– year: 2017
– volume: 386
  start-page: 2287
  year: 2015
  end-page: 2323
  article-title: Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013
  publication-title: Lancet
– volume: 13
  start-page: 755
  year: 2004
  end-page: 779
  article-title: Exploratory data analysis for complex models
  publication-title: J. Computnl Graph. Statist.
– volume: 367
  start-page: 4361
  year: 2009
  end-page: 4383
  article-title: Statistical inference for exploratory data analysis and model diagnostics
  publication-title: Phil. Trans. R. Soc. Lond.
– volume: 19
  start-page: 555
  year: 2017
  article-title: The prior can often only be understood in the context of the likelihood
  publication-title: Entropy
– year: 2013
– volume: 102
  start-page: 460
  year: 2014
  article-title: The statistical crisis in science: data‐dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up
  publication-title: Am. Scient.
– volume: 27
  start-page: 1413
  year: 2017b
  end-page: 1432
  article-title: Practical Bayesian model evaluation using leave‐one‐out cross‐validation and WAIC
  publication-title: Statist. Comput.
– volume-title: Bayesian Data Analysis
  year: 2013
  ident: 2023030719372669700_
  doi: 10.1201/b16018
– volume: 2
  start-page: 1360
  year: 2008
  ident: 2023030719372669700_
  article-title: A weakly informative default prior distribution for logistic and other regression models
  publication-title: Ann. Appl. Statist.
  doi: 10.1214/08-AOAS191
– volume: 386
  start-page: 2287
  year: 2015
  ident: 2023030719372669700_
  article-title: Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013
  publication-title: Lancet
  doi: 10.1016/S0140-6736(15)00128-2
– volume: 13
  start-page: 755
  year: 2004
  ident: 2023030719372669700_
  article-title: Exploratory data analysis for complex models
  publication-title: J. Computnl Graph. Statist.
  doi: 10.1198/106186004X11435
– volume: 102
  start-page: 460
  year: 2014
  ident: 2023030719372669700_
  article-title: The statistical crisis in science: data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up
  publication-title: Am. Scient.
  doi: 10.1511/2014.111.460
– start-page: 147
  volume-title: Bayesian Statistics 4
  year: 1992
  ident: 2023030719372669700_
  doi: 10.1093/oso/9780198522669.003.0009
– volume: 367
  start-page: 4361
  year: 2009
  ident: 2023030719372669700_
  article-title: Statistical inference for exploratory data analysis and model diagnostics
  publication-title: Phil. Trans. R. Soc. Lond.
– volume-title: loo: efficient leave-one-out cross-validation and WAIC for Bayesian models
  year: 2017
  ident: 2023030719372669700_
– volume-title: Pareto smoothed importance sampling
  year: 2017
  ident: 2023030719372669700_
– volume-title: A conceptual introduction to Hamiltonian Monte Carlo
  year: 2017
  ident: 2023030719372669700_
– volume: 32
  start-page: 1
  year: 2017
  ident: 2023030719372669700_
  article-title: Penalising model component complexity: a principled, practical approach to constructing priors
  publication-title: Statist. Sci.
  doi: 10.1214/16-STS576
– volume-title: RStan: the R interface to Stan
  year: 2017
  ident: 2023030719372669700_
– volume-title: Stan Modeling Language User's Guide and Reference Manual
  year: 2017
  ident: 2023030719372669700_
– volume: 19
  start-page: 555
  year: 2017
  ident: 2023030719372669700_
  article-title: The prior can often only be understood in the context of the likelihood
  publication-title: Entropy
  doi: 10.3390/e19100555
– volume-title: bayesplot: plotting for Bayesian models
  year: 2017
  ident: 2023030719372669700_
– volume: 80
  start-page: 5581
  year: 2018
  ident: 2023030719372669700_
  article-title: Yes, but did it work?: Evaluating variational inference
  publication-title: Proc. Mach. Learn. Res.
– volume-title: ggplot2: Elegant Graphics for Data Analysis
  year: 2009
  ident: 2023030719372669700_
  doi: 10.1007/978-0-387-98141-3
– volume: 68
  start-page: 231
  year: 2018
  ident: 2023030719372669700_
  article-title: Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution
  publication-title: Appl. Statist.
– volume: 27
  start-page: 1413
  year: 2017
  ident: 2023030719372669700_
  article-title: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
  publication-title: Statist. Comput.
  doi: 10.1007/s11222-016-9696-4
– volume-title: R: a Language and Environment for Statistical Computing
  year: 2017
  ident: 2023030719372669700_
SSID ssj0000077
Score 2.6633675
Snippet Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains....
SourceID proquest
crossref
wiley
jstor
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 389
SubjectTerms Bayesian analysis
Bayesian data analysis
Data analysis
Iterative methods
Markov chains
Original Articles
Statistical graphics
Statistical workflow
Visualization
Workflow
Title Visualization in Bayesian workflow
URI https://www.jstor.org/stable/48556420
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frssa.12378
https://www.proquest.com/docview/2167170357
Volume 182
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEB5KT_Xgo1qsVgnqRSFlm-w2CXipYilCPbRWepGwTyiWVpoW0V_v7uZhKyLoKTlMXjM7u9-Eb74FuMAYSe4J6iKBmIv1IuEyXUi4bS9EDPki4pZV2X9o90b4fkzGJbjOe2FSfYjih5vJDDtfmwSnLFlL8kWS0KaedwPT6WvIWgYRDda0o5DddlFDdGzYFGGmTWpoPF-XbqxGKSFxA2quA1a74nR34Dl_15Ro8tJcLVmTf3yTcfzvx-zCdgZFnU46dvagJGdV2OoXOq5JFSoGi6ZSzvtw9jRJTAtm2rjpTGbODX2XpgnTMewuNZ2_HcCoe_d423OzLRZcbrS_XO03T1FMIolYFCjCqS7AfIm4x5X0sdQHFQjRUogGLOABE0JJTzJhdGM8LPwalGfzmTwEJyIYK1-GnDCKOSZhRDyiRBvrLFeMojpc5q6OeaY_brbBmMZ5HWKcEFsn1OG8sH1NVTd-tKrZiBUmRuhG11P6UY08hHGWkknstdq6dEU-CepwZWPxy63jwXDYsWdHfzE-hooGVFHK6m5AeblYyRMNWpbs1A7OT5xL5vo
linkProvider Wiley-Blackwell
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NT8JAEJ0oHtSD30QUtVEvmpQs7S5tj2gkqOBB0Xhrul8JkYChEKO_3p1tQTDGRE_tYdqmuzs7bzZv3gCcUkqU8GTiEkm4S02QcLlJJNyaFxJOfBkJy6ps39Waj_TmmT3n3Byshcn0IaYHbugZdr9GB8cD6RkvH6ZpUjEbbxAuwhK29LYZ1f2MehSxjRcNSKfIpwhzdVIk8nw9OxePMkriHNichaw25jTWs8aqqZUqRKrJS2U84hXx8U3I8d-_swFrORp16tny2YQF1d-C1fZUyjXdghWEo5ma8zYcP3VTrMLMajedbt-5SN4V1mE6SPDSvcHbDjw2rjqXTTfvsuAKlP9yqyaE6YSySBEeBZqJxORgviLCE1r5VJmLDqSsapIEPBABl1IrT3GJ0jEelX4RCv1BX-2CEzFKta9CwXhCBWVhxDymZY0aR9c8ISU4m4x1LHIJcuyE0YsnqQgOQmwHoQQnU9vXTHjjR6uinbKpCWrdmJTKfKo8mcM498o09qo1k70SnwUlOLeT8cur4_uHh7q92_uL8REsNzvtVty6vrvdhxWDr6KM5F2Gwmg4VgcGw4z4oV2pnw4Q6xU
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZS8NAEB5qBakP3sV6BvVFIWWb7OYAX7xKPVqktdIXCdkLilLFtIj-enc3SW1FBH1KHibX7M7ON-GbbwEOMEaCOTy2EUfUxipJ2FQVErbnBIgil4fMsCqbLa_RxVc90ivAcd4Lk-pDjH-46cgw67UO8BcuJ4L8NUniqlp3_WAGZrGHAj2nz9sT4lHI7LuoMDrWdIogEyfVPJ6va6fSUcpInMKak4jVpJz6IjzkL5syTR6royGtso9vOo7__ZolWMiwqHWSTp5lKIjBCsw3x0KuyQqUNBhNtZxXYe--n-gezLRz0-oPrNP4XeguTEvTu-TT89sadOsXd2cNO9tjwWZa_MuuqQQmY0xCgWjoS8JiVYG5AjGHSeFioQ7S57wmUexTn_mUcykcQbkWjnEwd8tQHDwPxDpYIcFYuiJghMaYYRKExCGSe1iFuaQxqsBh7uqIZQLkeh-MpygvRLQTIuOECuyPbV9S2Y0frcpmxMYmWulGFVTqUVv5EEZZTCaRU_NU7Ypc4lfgyIzFL7eO2p3OiTnb-IvxLszdntejm8vW9SaUFLgKU4b3FhSHryOxrQDMkO6YefoJeyjpzQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Visualization+in+Bayesian+workflow&rft.jtitle=Journal+of+the+Royal+Statistical+Society.+Series+A%2C+Statistics+in+society&rft.au=Gabry%2C+Jonah&rft.au=Simpson%2C+Daniel&rft.au=Vehtari%2C+Aki&rft.au=Betancourt%2C+Michael&rft.date=2019-02-01&rft.pub=Wiley&rft.issn=0964-1998&rft.eissn=1467-985X&rft.volume=182&rft.issue=2&rft.spage=389&rft.epage=402&rft_id=info:doi/10.1111%2Frssa.12378&rft.externalDocID=48556420
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0964-1998&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0964-1998&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0964-1998&client=summon