BayesianInference

The Bayesian inference process:

1. Specify a prior probability distribution reflecting our knowledge of the problem.

2. Collect Data.

3. Calculate the Likelihood: how well the observed data aligns with each possible hypothesis or model.

4. Apply Bayes’ Theorem: Bayes’ theorem is used to update the prior probabilities into posterior probabilities, considering the data and the likelihood.

5. Make Informed Decisions: The posterior probabilities provide updated beliefs about the hypotheses, models, or parameters. We can use these posterior probabilities to make decisions, make predictions, or perform further analysis.

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Here, we study robust decision-making in machine learning algorithms using Bayesian Inference.

Reference: Understanding bayesian inference