while my_mcmc: gently(samples) - Hierarchical Bayesian Neural Networks with Informative Priors
Hierarchical Bayesian Neural Networks with Informative Priors 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 how likely a user will buy a certain product. There could be thousands of products b
Why hierarchical models are awesome, tricky, and Bayesian
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 Regression in PyMC”, written with Danne Elbers. See also my interview with FastForwardLabs whe
Bayesian Deep Learning Part II: Bridging PyMC3 and Lasagne to build a Hierarchical Neural Network — While My MCMC Gently Samples
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 Neural Networks. As you may know, PyMC3 is also using Theano so having the Artifical Neural Network (ANN) be built in Lasagne, but
while my_mcmc: gently(samples) - Bayesian Deep Learning
Variational Inference: Bayesian Neural Networks 2016-2018 by Thomas Wiecki, updated by Maxim Kochurov Original blog post: https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/ Current trends in Machine Learning 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 us
while my_mcmc: gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC
As you can see, we have multiple radon measurements (log-converted to be on the real line) in a county and whether the measurement has been taken in the basement (floor == 0) or on the first floor (floor == 1). Here we want to test the prediction that radon concentrations are higher in the basement. The Models Pooling of measurements Now you might say: “That’s easy! I’ll just pool all my data and
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while my_mcmc: gently(samples) - Using Bayesian Decision Making to Optimize Supply Chains