Eric J Ma's Website

I've been reflecting on the way I learned statistics, and I think I learned it in a flawed fashion. In response to this reflection, I've began reworking an introduction to Bayesian statistical inference that focuses on statistical story telling. Read on to find out more!

What gives us the logical leap from a joint distribution $P(A, B)$ in abstract, to the more tangible $P(H, D)$ (for hypothesis and data? Here's an excerpt from a tutorial in the making about Bayesian statistics.

How in the world did "inference" come to mean "prediction" amongst deep learners? I have no idea, but I do have a rant.

There's a pile of free books released for everybody! One of them caught my eye: the Data Science Design Manual. Link in the full post!

A mini-advertisement, completely unpaid and totally unsolicited, for the use of remote "development" containers on VSCode, with a little pro-tip about pre-building containers sprinkled in for good measure!

I recently stumbled upon a package that makes animations dead simple. Check it out!

In this blog post, I'll show you what lambda expressions are, and where we might use them in a data science setting.

Are file paths driving you crazy in your data science project repository? Come read how you can use pyprojroot and Python’s pathlib to help make life a tad easier. You’ll love this new tool in your toolkit!

Here's the motivation behind my latest essay explaining Markov models and their variants, as well as how to perform Bayesian inference on them.

During the COVID-19 outbreak period, you might find yourself with a good chunk of time to pick up Python. It's an incredibly productive language to learn! At the same time, the wealth of resources out there can be intimidating. Here's my opinionated list of resources in 2020 that could be handy for you. And if you have others, be sure to share!