# Bayes Ia

Notes for an advanced course on Bayesian statistics
Alternatives To Bayes Ia
Probabilistic Programming And Bayesian Methods For Hackers25,288
a month ago196mitJupyter Notebook
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Pgmpy2,295657 days ago18June 30, 2022238mitPython
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Bayesian Modelling In Python2,202
6 years agoJupyter Notebook
A python tutorial on bayesian modeling techniques (PyMC3)
Imodels1,0682a day ago26July 03, 202223mitJupyter Notebook
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Easystats880
2 days ago43gpl-3.0R
:milky_way: The R easystats-project
Statistical Rethinking With Python And Pymc3674
5 years ago2Jupyter Notebook
Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath
Jasp Desktop610
4 days ago23agpl-3.0C++
JASP aims to be a complete statistical package for both Bayesian and Frequentist statistical methods, that is easy to use and familiar to users of SPSS
Pydlm400112 years ago13December 19, 201829bsd-3-clausePython
A python library for Bayesian time series modeling
Elfi247
14 days ago6June 13, 202210bsd-3-clausePython
ELFI - Engine for Likelihood-Free Inference
Bat.jl163
15 days ago17otherJulia
A Bayesian Analysis Toolkit in Julia
Alternatives To Bayes Ia
Select To Compare

Alternative Project Comparisons

Data don't speak for themselves

# Description

Bayesian statistics is rising in popularity in the astrophysical literature. It is no longer a debate: "work in Bayesian statistics now focuses on applications, computations, and models. Philosophical debates [...] are fading to the background" (Bayesian Data Analysis, Gelman et al.). This is happening for two main reasons: faster computers and more complex models. In order to keep up, it is important to understand the fundamentals of Bayesian statistics, but it is as important to know how to deal with data analysis applications.

In this course I want to provide a brief introduction to advanced concepts in Bayesian statistics. Emphasis will be on "intuition" and "computation". No coin tossing, only real applications that relate to our day-to-day problems.

# Plan for the course

The idea is to present some statistical results in an intuitive manner and then turn to computational methods. By the end of the course, the students should be able to understand:

THEORY

• what are the main differences between frequentist and Bayesian statistics . problems with p-values, confidence intervals and null hypothesis testing
• the basic rules of probability theory
• how to assign probability distributions . the role of priors . the likelihood
• the simplest models in Bayesian statistics: . linear regression, . beta-binomial model, . hierarchical models

PRACTICE

• what is an MCMC, how it works and why it works; shortcomings (and alternatives)
• code an MCMC from scratch to sample a distribution
• for the (weighted) linear regression model . build the probabilistic graphical model and derive the posterior distribution . use our MCMC to sample the posterior for slope and intercept . solve the problem with a "black box" MCMC sampler in Python
• calculate the evidence integral to do model comparison

Popular Statistics Projects
Popular Bayesian Projects
Popular Data Processing Categories

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Tex
Course
Statistics
Probability
Bayesian
Mcmc
Bayesian Statistics