# Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders - PhD thesis

I noticed that as my personal website at my former university went down that my PhD thesis could not be found anywhere, so I'm posting it here.

During my PhD I explored how machine learning and computational modeling of the brain can be used to improve our understanding, and diagnostics …

# My foreword to "Bayesian Analysis with Python, 2nd Edition" by Osvaldo Martin

When Osvaldo asked me to write the foreword to his new book I felt honored, excited, and a bit scared, so naturally I accepted. What follows is my best attempt to convey what makes probabilistic programming so exciting to me. Osvaldo did a great job with the book, it is …

# Using Bayesian Decision Making to Optimize Supply Chains

(c) 2019 Thomas Wiecki & Ravin Kumar

As advocates of Bayesian statistics in data science we often have to convince business-minded colleagues or customers of the added value of such an approach. While there are many good reasons for applying Bayesian modeling to solve business problems (Sean J Taylor recently had …

# Hierarchical Bayesian Neural Networks with Informative Priors

(c) 2018 by Thomas Wiecki

Imagine you have a machine learning (ML) problem but only small data (gasp, yes, this does exist). This often happens when your data set is nested -- you might have many data points, but only few per category. For example, in ad-tech you may want predict …

# 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 …

# Variational Inference: Bayesian Neural Networks¶

(c) 2016-2018 by Thomas Wiecki, updated by Maxim Kochurov

Original blog post: https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/

There are currently three big trends in machine learning: Probabilistic Programming, Deep Learning and "Big Data". Inside of PP …