Charles D Lindsey
Charles Lindsey is a Principal Data Scientist at Revionics. Charles earned a PhD in Statistics from Texas A&M in 2010, where he researched dimension reduction and classification. Charles then worked at StataCorp LLC. At StataCorp, Charles was the lead developer of the Extended Regression Model (ERM) commands, which allow causal inference on observational data with common complications like unobserved confounding variables and sample selection. At Revionics, Charles works on price optimization and sales forecasting using Bayesian methods and other machine learning techniques.
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Sessions
TensorFlow Probability is a powerful library for statistical analysis in Python. Using TensorFlow Probability’s implementation of Bayesian methods, modelers can incorporate prior information and obtain parameter estimates and a quantified degree of belief in the results. Resampling methods like Markov Chain Monte Carlo can also be used to perform Bayesian analysis. As an alternative, we show how to use numerical optimization to estimate model parameters, and then show how numerical differentiation can be used to get a quantified degree of belief. How to perform simulation in Python to corroborate our results is also demonstrated.