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 ...

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 ...

: 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 ...

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 ...