This blog post describes a sequence of 9 steps to set up a reproducible workflow for scientific writing based on open-source tooling. It boils down to writing the manuscript in Rmarkdown, and using a set of auxiliary tools to manage citations and output to Word to share with collaborators and to prepare the final document for submission to the journal.
This blog post concerns a famous toy problem in Reinforcement Learning, the FrozenLake environment. We compare solving an environment with RL by reaching maximum performance versus obtaining the true state-action values $Q_{s,a}$.
In this blogpost, we solve a famous sequential decision problem called Jacks Car Rental by first turning it into a Gym environment and then use a RL algorithm called Policy Iteration (a form of Dynamic Programming) to solve for the optimal decisions to take in this environment.
Here we show how to use Stan and the brms R-package to calculate the posterior predictive distribution of a covariate-adjusted average treatment effect (ATE).
With the commoditization of deep learning in the form of Keras, I felt it was about time that I jumped on the Deep Learning bandwagon.
This blog post is on simulating fake data using the R package simstudy. Motivation comes from my interest in converting real datasets into synthetic ones.
In this post, we’ll explore the BupaR suite of Process Mining packages created by Gert Janssenswillen of Hasselt University.
In this blog post, I describe the introductory course on Causal Inference I pieced together using various materials available online. It combines Pearl’s Causal Graph approach with statistics Gelman/mcElreath style.
In this blog post, we explore how to implement the validation set approach in caret. This is the most basic form of the train/test machine learning concept.
In this post, I show how to create a Arduino-based atmospheric sensor prototype capable of storing large amounts of data on a microSD card.