Look Who it Was Random video

Vincent D. Warmerdam - SaaaS - Sampling as an Algorithm Service

3,627 views · Published 13 June 2017 · 38:59 · Indexed 23 October 2026

Channel: PyData · 2017 · Science & Technology

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Filmed at PyData London 2017

Description
In this talk I will explain the idea of sampling to get to your model and I will demonstrate it with examples. The goal is to start with a for loop and to end with understanding how MCMC algorithms work.

Abstract
A lot of people understand the scikit-learn models of todays world but feel uneasy about the whole MCMC method of training. Why are these algorithms different? How is it that you don't use a gradient method but a sampler instead? It can feel a bit misterious if you've not properly been introduced to this other way of thinking.

In this talk I will explain the idea of sampling to get to your model and I will demonstrate it with examples. The goal is to start with a for loop and to end with understanding how MCMC algorithms work. As a consequence the audience will also get a proper introduction to PyMC3. In particular I will discuss the following;

why markov chain sampling can be equivalent to direct sampling
how to build your own MCMC sampler with a for loop
how this for loop can be run faster by using PyMC3 instead
the key idea of inference and how i was briefly able apply it in a santa kaggle competition
how to analyse timeseries with MCMC in PyMC3
Parts of this talk are readily available on my blog;

http://koaning.io/switching-to-sampli...
http://koaning.io/elimination-via-inf...

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