Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis

• Published in: Biometrics Vol. 79; no. 2; pp. 629 - 641
• Main Authors: Yu, Cheng‐Han, Li, Meng, Noe, Colin, Fischer‐Baum, Simon, Vannucci, Marina
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2023
• Subjects: Aged; Bayes Theorem; Bayesian analysis; Bayesian inference; Bayesian theory; Data analysis; derivatives; EEG; Electroencephalography; ERP data; Evoked Potentials; Gaussian process; Humans; Machine learning; Mathematical models; Neuroimaging; Normal Distribution; Perception; Regression analysis; Regression models; semiparametric; Speech; Speech perception; Statistical inference; Temporal perception; Young Adult; derivatives; ERP data; Gaussian process; Bayesian inference; semiparametric

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, whi...