I am a fan of the book Statistical Rethinking, so I port the codes of its second edition to NumPyro.I hope that the book and this translation will be helpful not only for NumPyro/Pyro users but also for ones who are willing to do Bayesian statistics in Python. - StatisticalRethinkingJulia. Statistical Rethinking with PyTorch and Pyro. GitHub; Kaggle; Posts; Twitter; 11 min read Statistical Rethinking: Week 1 2020/04/19. Statistical Rethinking with PyTorch and Pyro. This one got a thumbs up from the Stan team members who’ve read it, and Rasmus Bååth has called it “a pedagogical masterpiece.” The book’s web site has two sample chapters, video tutorials, and the code. Lecture 04 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. We looked at Metropolis, Gibbs and finally HMC. Interactions < Chapter 6. Statistical Rethinking (2nd Ed) with Tensorflow Probability. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. The draft is no longer available here for download. GitHub is where people build software. I will also post problem sets and solutions here. README.md Browse package contents. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Leave a Reply Cancel reply. Section 5.1: Spurious association. Markov Chain Monte Carlo > In [0]: import itertools import math import pandas as pd import seaborn as sns import torch import pyro import pyro.distributions as dist import pyro.ops.stats as stats from rethinking import LM, MAP, coef, … 04-Dec 14: Wiggly Orbits