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Yao, Yuling; Vehtari, Aki; Gelman, Andrew – Grantee Submission, 2022
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…
Descriptors: Bayesian Statistics, Computation, Markov Processes, Monte Carlo Methods
Natesan, Prathiba; Hedges, Larry V. – Grantee Submission, 2016
Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in SCDs, no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in…
Descriptors: Bayesian Statistics, Statistical Inference, Research Design, Models