Probabilistic Modeling
At the heart of probabilistic modeling is the idea that because data is random, and so follows a probability distribution, our models of that data must also follow a probability distribution and be probabilistic models from the outset. To understand how to build those models, we must first understand the probability distribution that the data follows. From this, we can calculate the distribution that our model parameters follow by using one of the most famous theorems in probability theory. To do all of this, we will cover the following topics:
- Likelihood: In this section, we will learn about the probability distribution of the data given a model
- Bayes’ theorem: In this section, we will learn how to work with conditional probabilities and calculate the probability of a model given the data
- Bayesian modeling: In this section, we will learn how to use the probability of the model given the data to make useful inferences
- Bayesian modeling...
