Bayesian statistics have made great strides in recent years, developing a class of methods for estimation and inference via stochastic simulation known as Markov Chain Monte Carlo (MCMC) methods. MCMC ...

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, ...

Bayesian Inference: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and ...

For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here's a practical guide to how Bayes ...

Peida Zhan, Hong Jiao, Kaiwen Man, Lijun Wang, Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial, Journal of Educational and Behavioral Statistics, Vol. 44, No. 4 (August 2019), pp. 473 ...

The increasing interest in Bayesian group sequential design is due to its potential to reinforce efficiency in clinical trials, shorten drug development time, and enhance the accuracy of statistical ...

Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control.

Bayesian Statistics and Inference Publication Trend The graph below shows the total number of publications each year in Bayesian Statistics and Inference.