What is Bayesian probability?

Bayesian probability involves updating the probability of a hypothesis based on new evidence. It is founded on Bayes' theorem, which mathematically describes how to revise these probabilities. Here's a concise explanation based on trusted experts:

Basic Concept

• Bayes' Theorem: It's a rule in probability theory used to update the probability of a hypothesis as more evidence or information becomes available 1.
• Prior Probability: An initial probability estimate before new evidence is taken into account.
• Posterior Probability: The revised probability after considering the new evidence.
• The theorem can be summarized as: [ P(H|E) = \frac{P(E|H) \cdot P(H)}{P(E)} ] where ( P(H|E) ) is the posterior probability, ( P(E|H) ) is the likelihood, ( P(H) ) is the prior probability, and ( P(E) ) is the marginal likelihood of the evidence 2.

Practical Application

Bayesian reasoning helps us revise our confidence in a particular hypothesis when confronted with new evidence. For example:

• Example 1: Assessing the probability of rain given wet streets. The likelihood term would be the probability of seeing wet streets if it were raining 2.
• Example 2: Determining if a coin is fair based on the observed outcomes of multiple coin flips. The prior might be influenced by the general belief that coins are usually fair, and the posterior is updated based on the flip outcomes 1.

Benefits

• Flexibility: It allows for flexible updating of beliefs as new data comes in, making it very dynamic 3.

  Bayesian Probabilistic Modeling

  Guy Emerson

  Walid Saba

  Keith Duggar

  Keith discusses the concept of an ap distribution, where probabilities themselves can be probabilistic. Guy emphasizes the importance of hierarchical probabilistic modeling. Walid raises the question of qualitative versus quantitative aspects in probability, adding depth to the discussion.

  

  Machine Learning Street Talk (MLST)

  #062 - Dr. Guy Emerson - Linguistics, Distributional Semantics

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• Avoids False Certainty: By incorporating new evidence continuously, Bayesian probability helps in avoiding false certainty in complex situations 3.
• Hierarchy: It can handle hierarchical probabilistic modeling, useful in complex systems where probabilities themselves are nested 1.

Thus, Bayesian probability is a powerful method for updating beliefs and making decisions based on evolving evidence.