# My small corner of the internet

# Bootstrapped Regression Coefficients

Here we explore the theoretical coefficient distributions from a linear regression model. When fitting a regression model we can get estimates for the standard deviation of the coefficients. We use bootstrapping to get an empiracle distribution of the regression coefficients to compare against those distributions.

# Exploring parquet datasets

Parquet files are a columinar data format we can use to store dataframes. They can be stored in partitions, which can allow us to load only a subset of the data. This is useful is we are filtering the data, as we can do that without loading it all into memory.

# Fitting a Distribution with Pyro: Part 2 - Beta

This follows on from the previous post on fitting a gaussian distribution with pyro:
Fitting a Distribution with Pyro

# Fitting a Distribution with Pyro

In this simple example we will fit a Gaussian distribution to random data from a gaussian with some known mean and standard deviation.
We want to estimate a distribution that best fits the data using variational inference with Pyro.

# NBA Match Analysis

The aim of this project is to plot interactive scores of NBA games over the course of the match: