Data Mining Using Bayesian Data Analysis and Python

Thomas Wiecki will present a talk title 'Bayesian Data Analysis with PyMC3'

Details:

Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance.

PyMC3 is a new Python module that features next generation sampling algorithms and an intuitive model specification syntax. The whole code base is written in pure Python and Just-in-time compiled via Theano for speed.

In this talk I will provide an intuitive introduction to Bayesian statistics and how probabilistic models can be specified and estimated using PyMC3.

BIO:
Thomas Wiecki is currently enrolled in the Ph.D. program at Brown University where he investigates the neuronal underpinnings of mental illness using quantitative methods like Bayesian Modeling. He also works as a quantitative researcher for Quantopian Inc where he helps building the worlds' first browser based financial backtesting platform.