# 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

There are currently three big trends in machine learning: Probabilistic Programming, Deep Learning and "Big Data". Inside of PP, a lot of innovation is in making things scale using Variational Inference. In ...

# 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 ...

# 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 ...

# The Best Of Both Worlds: Hierarchical Linear Regression in PyMC3

Authors: Danne Elbers, Thomas Wiecki

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

The power of Bayesian modelling really clicked for me when I ...

# 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 ...

# This world is far from Normal(ly distributed): Bayesian Robust Regression in PyMC3

Author: Thomas Wiecki

This tutorial first appeard as a post in small series on Bayesian GLMs on my blog:

# The Inference Button: Bayesian GLMs made easy with PyMC3

Author: Thomas Wiecki

This tutorial appeared as a post in a small series on Bayesian GLMs on my blog: