Results 241 to 250 of about 54,345 (310)

Flexible yet Sparse Bayesian Survival Models With Time-Varying Coefficients and Unobserved Heterogeneity. [PDF]

Stat Med
Knaus P, Winkler D, Schoppmann SF, Jomrich G.   +3 more
europepmc   +1 more source

Power priors for latent variable mediation models under small sample sizes

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Latent variable models typically require large sample sizes for acceptable efficiency and reliable convergence. Appropriate informative priors are often required for gainfully employing Bayesian analysis with small samples. Power priors are informative priors built on historical data, weighted to account for non‐exchangeability with the ...
Lihan Chen, Milica Miočević, Carl F. Falk   +2 more
wiley   +1 more source

In Vitro Analysis and Dynamic Modeling of SARS-CoV-2 Infection Inhibition by Sigma-1 Receptor Antagonist PB28. [PDF]

Bull Math Biol
Lisowski B, V Rezelj V, Vignuzzi M, Abate C, Bernhauerová V.   +4 more
europepmc   +1 more source

Multi-purpose Program for Social System Analysis 33- Maximum Likelihood Method and MCMC - [PDF]

, 2018
正康 福井
openalex  

Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time‐varying Q matrices, latent attributes, item parameters, attribute class proportions and transition ...
Chen‐Wei Liu
wiley   +1 more source

Modeling insurance claims using Bayesian nonparametric regression. [PDF]

PLoS One
Shams M, Ghosh K.
europepmc   +1 more source

LLM‐based prior elicitation for Bayesian graphical modeling

British Journal of Mathematical and Statistical Psychology, EarlyView.
ABSTRACT In the Bayesian graphical modeling framework, priors on network structure encode theoretical assumptions and uncertainty about the topology of psychological constructs under study. For instance, the Bernoulli prior specifies the probability of each pairwise interaction, the Beta–Bernoulli prior governs expected network density, and the ...
Nikola Sekulovski, Meike Waaijers, Giuseppe Arena   +2 more
wiley   +1 more source

Computational protocol for hierarchical Bayesian modeling of perception and generalization in fear conditioning. [PDF]

STAR Protoc
Yu K, Vanpaemel W, Tuerlinckx F, Zaman J.   +3 more
europepmc   +1 more source

Post-Monte Carlo B: Structural Adaptation of Non-Stationary Proposal Distributions– From Geometric Awareness to Mode-Driven MCMC Evolutionary Dynamics

changzheng zhou, ziqing zhou
openalex   +1 more source

To vary or not to vary: A flexible empirical Bayes factor for testing variance components

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Random effects are the gold standard for capturing structural heterogeneity, such as individual differences or temporal dependence. Yet testing their presence is difficult because variance components are constrained to be non‐negative, creating a boundary problem. This paper introduces a flexible empirical Bayes factor (EBF) for testing random
Fabio Vieira, Hongwei Zhao, Joris Mulder
wiley   +1 more source