Bayesian Reasoning

StatisticsDecision MakingProbability

Bayesian Reasoning updates probabilities based on new evidence using Bayes' Theorem.

Introduction

Bayesian Reasoning updates probabilities based on new evidence using Bayes' Theorem.

Core Concepts

  1. Prior Probability: Initial belief or probability.
  2. Likelihood: Probability of observing evidence given the prior.
  3. Posterior Probability: Updated belief after considering evidence.

Applications

  • Medical diagnostics for refining probabilities of conditions.
  • Machine learning for probabilistic modeling.

Related Resources

  • Tools: Bayesian inference libraries like PyMC3.
  • Book: "Bayesian Data Analysis" by Andrew Gelman.

Related Topics

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