In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

: The posterior; the probability of the hypothesis (e.g. that a parameter has a certain value) given the data: The likelihood of observing/generating the data given the hypothesis: The prior ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Approximate Bayesian computation (ABC) algorithms are a class of Monte Carlo methods for doing inference when the likelihood function can be simulated from, but not explicitly evaluated. This ...

This tutorial demonstrates how to conduct a stratified Bayesian survival analysis using a discrete-time beta-binomial approach, as described in the paper: Bayesian Stratified Analysis of Treatment ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...