I picked this little book up at a railway station for two reasons: as a trainer, I wanted to find … Morecontinue reading.
Category: Machine learning
In the post https://statcompute.wordpress.com/2019/04/27/more-general-weighted-binning, I’ve shown how to do the weighted binning with the function wqtl_bin() by the iterative partitioning. However, the outcome from wqtl_bin() sometimes can be too coarse....continue reading.
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.