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Printable Handouts
Navigable Slide Index
- Introduction
- The Goal of Inference
- Data Aspects of Marketing Problems
- The Likelihood Principle
- Bayes Theorem
- Summarizing the Posterior
- Prediction (1)
- Decision Theory
- Is Sampling Theory Useful?
- Sampling Properties of Bayes Estimators
- Bayes Inference: Summary
- Bayes and Classical Estimators (1)
- Bayes and Classical Estimators (2)
- Benefits and Costs of Bayes Inference
- There’s No Theorem Like Bayes Theorem
- Bayesian Computations
- Conjugate Families
- Beta-Binomial Model
- Beta Distribution
- Posterior (1)
- Prediction (2)
- Regression Model (1)
- Regression Model (2)
- Conjugate Prior
- Geometry of Regression
- Regression Likelihood (1)
- Regression Likelihood (2)
- Bayesian Regression
- Posterior (2)
- Simulating from Joint
- IID Simulations (1)
- IID Simulations (2)
- Shrinkage and Conjugate Priors
- Runireg (1)
- Runireg (2)
- Runireg: Output (1)
- Runireg: Output (2)
- Summary
Topics Covered
- The goal of inference
- Bayes theorem
- Decision theory
- Sampling properties of Bayes estimators
- Costs and benefits of Bayes
- Conjugate families
- Beta-binomial model
- Bayesian regression
- IID simulator
- Shrinkage and conjugate priors
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Talk Citation
Rossi, P. (2010, January 27). Bayesian essentials and bayesian regression [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved August 31, 2024, from https://doi.org/10.69645/SYUR4047.Export Citation (RIS)
Publication History
Bayesian essentials and bayesian regression
Published on January 27, 2010
34 min