Search Results - "Rosato, Conor" :: K.UTB vyhledávací portál

Simulation to optimize the laboratory diagnosis of bacteremia

by Gerada, Alessandro, Roberts, Gareth, Howard, Alex, Reza, Nada, Velluva, Anoop, Rosato, Conor, Green, Peter L, Hope, William
Published in Microbiology spectrum (24.09.2024)

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Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review

by Rosato, Conor, Green, Peter L., Harris, John, Maskell, Simon, Hope, William, Gerada, Alessandro, Howard, Alex
Published in IEEE access (2024)

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Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

by Rosato, Conor, Moore, Robert E, Carter, Matthew, Heap, John, Harris, John, Storopoli, Jose, Maskell, Simon
Published in Information (Basel) (01.03.2023)

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Refining epidemiological forecasts with simple scoring rules

by Moore, Robert E, Rosato, Conor, Maskell, Simon
Published in Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences (03.10.2022)

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Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters

by Rosato, Conor, Devlin, Lee, Beraud, Vincent, Horridge, Paul, Schon, Thomas B., Maskell, Simon
Published in IEEE transactions on signal processing (2022)

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Enhanced SMC2: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

by Rosato, Conor, Murphy, Joshua, Varsi, Alessandro, Horridge, Paul, Maskell, Simon
Published in 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (04.09.2024)

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Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

by Rosato, Conor, Murphy, Joshua, Varsi, Alessandro, Horridge, Paul, Maskell, Simon
Year of Publication 24.07.2024

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An O(log2 N) SMC2 Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

by Rosato, Conor, Varsi, Alessandro, Murphy, Joshua, Maskell, Simon
Published in 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) (27.11.2023)

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$An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel$

An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

by Rosato, Conor, Varsi, Alessandro, Murphy, Joshua, Maskell, Simon
Year of Publication 21.11.2023

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Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

by Rosato, Conor, Harris, John, Panovska-Griffiths, Jasmina, Maskell, Simon
Published in 2022 25th International Conference on Information Fusion (FUSION) (04.07.2022)

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Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

by Rosato, Conor, Harris, John, Panovska-Griffiths, Jasmina, Maskell, Simon
Year of Publication 15.05.2022

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Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters

by Rosato, Conor, Beraud, Vincent, Horridge, Paul, Schön, Thomas B, Maskell, Simon
Published in arXiv.org (27.04.2022)

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$Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals$

Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

by Rosato, Conor, Murphy, Joshua, Varsi, Alessandro, Horridge, Paul, Maskell, Simon
Published in arXiv.org (24.07.2024)

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Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

by Rosato, Conor, Moore, Robert E, Carter, Matthew, Heap, John, Storopoli, Jose, Maskell, Simon
Year of Publication 15.12.2021

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$An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel$

An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

by Rosato, Conor, Varsi, Alessandro, Murphy, Joshua, Maskell, Simon
Published in arXiv.org (21.11.2023)

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Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

by Rosato, Conor, Harris, John, Panovska-Griffiths, Jasmina, Maskell, Simon
Published in arXiv.org (15.05.2022)

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Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

by Rosato, Conor, Moore, Robert E, Carter, Matthew, Heap, John, Storopoli, Jose, Maskell, Simon
Published in arXiv.org (15.12.2021)

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Paper

Simulation to optimize the laboratory diagnosis of bacteremia

Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review

Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

Refining epidemiological forecasts with simple scoring rules

Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters

Enhanced SMC2: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

An O(log2 N) SMC2 Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters

Enhanced SMC\(^2\): Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

An \(\mathcal{O}(\log_2N)\) SMC\(^2\) Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

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