bayesian statistics

We have hosted the application bayesian statistics in order to run this application in our online workstations with Wine or directly.

Quick description about bayesian statistics:

This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values.

Features:

• Hierarchical Models
• Bayesian Logistic Regression
• Bayesian Regression with Count Data: Poisson Regression
• Common Probability Distributions
• Markov Chain Monte Carlo (MCMC) and Model Metrics
• Model Comparison: Cross-Validation and Other Metrics

Programming Language: Julia.
Categories: