Category: Machine learning
This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level model describing tadpole mortality, which may be...continue reading.
I am doing two BayesCamp workshops in central London this summer: Statistical Analysis for Clinical Audit, 21 June [bookings] Data … Morecontinue reading.
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the...continue reading.
In my GitHub repository (https://github.com/statcompute/MonotonicBinning), multiple R functions have been developed to implement the monotonic binning by using either iterative discretization or isotonic regression. With these functions, we can run...continue reading.
Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a basic introduction to flows using tfprobability, an...continue reading.
Not everybody who wants to get into deep learning has a strong background in math or programming. This post elaborates on a concepts-driven, abstraction-based way to learn what it’s all...continue reading.
I am a co-organiser of the International Workshop on Computational Economics and Econometrics, taking place this year on 3-5 July … Morecontinue reading.
This method can discretize a variable taking into consideration the target variable, similar to what decision tree do but with gain ratio.continue reading.
In the previous post (https://statcompute.wordpress.com/2019/02/03/sobol-sequence-vs-uniform-random-in-hyper-parameter-optimization), it is shown how to identify the optimal hyper-parameter in a General Regression Neural Network by using the Sobol sequence and the uniform random generator...continue reading.