# What's new in PyMC3 3.1

We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via pip install pymc3 or via conda install -c conda-forge pymc3.

A lot is happening in PyMC3-land. One thing I am particularily proud of is the developer community we have …

# Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data

(c) 2017 by Thomas Wiecki -- Quantopian Inc.

Most problems solved by Deep Learning are stationary. A cat is always a cat. The rules of Go have remained stable for 2,500 years, and will likely …

# Why hierarchical models are awesome, tricky, and Bayesian

(c) 2017 by Thomas Wiecki

Hierarchical models are underappreciated. Hierarchies exist in many data sets and modeling them appropriately adds a boat load of statistical power (the common metric of statistical power). I provided an introduction to hierarchical models in a previous blog post: Best Of Both Worlds: Hierarchical Linear …

# Bayesian Deep Learning Part II: Bridging PyMC3 and Lasagne to build a Hierarchical Neural Network

(c) 2016 by Thomas Wiecki

Recently, I blogged about Bayesian Deep Learning with PyMC3 where I built a simple hand-coded Bayesian Neural Network and fit it on a toy data set. Today, we will build a more interesting model using Lasagne, a flexible Theano library for constructing various types of …

# Bayesian Deep Learning

## Neural Networks in PyMC3 estimated with Variational Inference¶

(c) 2016 by Thomas Wiecki

## Bayesian Neural Networks in PyMC3¶

### Generating data¶

First, lets generate some toy data -- a simple binary classification problem that's not linearly separable.

In [1]:
%matplotlib inline
import pymc3 as pm
import theano.tensor as T
import theano …

# MCMC sampling for dummies

When I give talks about probabilistic programming and Bayesian statistics, I usually gloss over the details of how inference is actually performed, treating it as a black box essentially. The beauty of probabilistic programming is that you actually don't have to understand how the inference works in order to build …

# A modern guide to getting started with Data Science and Python

Python has an extremely rich and healthy ecosystem of data science tools. Unfortunately, to outsiders this ecosystem can look like a jungle (cue snake joke). In this blog post I will provide a step-by-step guide to venturing into this PyData jungle.

What's wrong with the many lists of PyData packages …

# The best of both worlds: Hierarchical Linear Regression in PyMC3¶

Today's blog post is co-written by my student Danne Elbers who is doing her masters thesis with me on computational psychiatry using Bayesian methods. This post also borrows heavily from a Notebook by Chris Fonnesbeck.

The power of Bayesian modelling …

# Easily distributing a parallel IPython Notebook on a cluster

Have you ever asked yourself: "Do I want to spend 2 days adjusting this analysis to run on the cluster and wait 2 days for the jobs to finish or do I just run it locally with no extra work and just wait a week."

If so, this blog post …

# Hammer time: Nailing the emcee ensemble sampler onto PyMC

tl;dr: I hacked the emcee--The MCMC-Hammer ensemble sampler to work on PyMC models.

## Motivation¶

PyMC is an awesome Python module to perform Bayesian inference. It allows for flexible model creation and has basic MCMC samplers like Metropolis-Hastings. The upcoming PyMC3 will feature much fancier samplers like Hamiltonian-Monte Carlo …