How to Use Bayesian Inference for Predictions in Python
Bayesian inference is a powerful statistical approach that allows you to update your beliefs about a hypothesis as new evidence becomes available. It provides a principled way to incorporate prior knowledge and uncertainty into your predictions. In this article, we will explore how to use Bayesian inference for making predictions in Python. We’ll cover the fundamental concepts, the mathematical formulation, and practical examples using Python libraries.
Understanding Bayesian Inference
Bayesian inference is based on the idea of updating our beliefs about a hypothesis as we observe new data. It allows us to quantify the uncertainty in our predictions by incorporating prior knowledge and the likelihood of the observed data.
The key components of Bayesian inference are:
- Prior distribution: The initial belief about the hypothesis before observing any data.
- Likelihood: The probability of observing the data given the hypothesis.
- Posterior distribution: The updated belief about the hypothesis after observing the data.
Bayesian inference combines these components using Bayes’ theorem to update our beliefs and make predictions.
