Ajit Jaokar has an interesting series. Here’s part one:

The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine learning. Often, books on machine learning combine the two approaches, or in some cases, take only one approach. This does not help from a learning standpoint.

So, in this two-part blog we first discuss the differences between the Frequentist and Bayesian approaches. Then, we discuss how they apply to machine learning algorithms.

Part two extends from there:

**Sampled from a distribution:** Many machine learning algorithms make assumptions that the data is sampled from a frequency. For example, linear regression assumes gaussian distribution and logistic regression assumes that the data is sampled from a Bernoulli distribution. Hence, these algorithms take a frequentist approach

My biases push me toward Bayesian approaches, and I really like what I see in Stan, but these techniques do often require a lot more processing power.