1 / 10

Bayesian Networks for Data Mining

Bayesian Networks for Data Mining. David Heckerman Microsoft Research (Data Mining and Knowledge Discovery 1, 79-119 (1997)). The Bayesian approach #1 Question What is Bayesian probability?. A person’s degree of belief in certain event. Personal (subjective)

remedios-dudley
Download Presentation

Bayesian Networks for Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Bayesian Networks for Data Mining David Heckerman Microsoft Research (Data Mining and Knowledge Discovery 1, 79-119 (1997))

  2. The Bayesian approach#1 QuestionWhat is Bayesian probability? • A person’s degree of belief in certain event. • Personal (subjective) • Your degree of belief that the coin will land heads.

  3. The Classical approach • Physical property of the world. • Repeated trials (frequency) • The probability that a coin will land heads.

  4. #2 QuestionWhat are the advantages and disadvantages of the Bayesian and classical interpretation of probability? Bayesian probability: + Reflects an expert’s knowledge. + Compiles with rules of probability • Arbitrary Classical probability: + Objective, unbiased. - Not available in most situations.

  5. Bayes Theorem Posterior = (likelihood X prior) / evidence

  6. Bayesian Networks • Graphical model that encodes the joint probability distribution (JPD) for a set of variables X. • It is a directed acyclic (not cyclic) graph. • Each node represents one variable and contains a set local probability distributions (LPD) associated with each variable.

  7. Bayesian Networks • Nodes • Parents • Children • Conditional probability tables • Construction

  8. Inference The computation of a probability of interest given a model is known as probabilistic inference P(X|e)=P(x,e)/P(e) = cP(X,e) Example on board.

  9. Learning • Learning from data • Refine the structure and LPD of a BN • Combine prior knowledge with data • Result: IMPROVED KNOWLEDGE

  10. Question #3Mention at least 3 advantages of Bayesian Networks for data analysis. Explain each one. • Handle incomplete data sets • Learning about causal relationships • Combine domain knowledge + data • Avoid over fitting.

More Related