Welcome to Decision Theory for Analysts

Contents:

• Course Overview
  • Welcome to the Course!
• Elements of Decision Theory
  • States of Nature, Experiment, Actions, and Loss
  • Making a Decision
  • Strategies, Average Losses, and Expected Loss
• Basic Probability
  • What is a Probability?
  • Probability Terms
  • Random Variables
  • Joint, Marginal, and Conditional Probability
  • Bayes’ Rule
• Utility Functions
  • Thought Experiment -
  • Utility Theory
  • St. Petersburg Paradox
  • Using Data to Infer Risk Tolerance
  • Examples
  • Alternative Utility Functions
• Bayes' Rule
  • Bayes’ Rule
  • A Young Example
  • A Discrete Case
• Bayes' Rule Discrete Example
  • What is our prior belief?
  • What is our likelihood?
  • What is the posterior?
  • What to do with Bayes’ Rule?
• Bayes' and Thompson Sampling
  • Back to the COVID example
  • Thompson Sampling
• Flexible Thompson Sampling
• Adding Drift to a Particle Filter
• Introduction to Markov Chains
  • Setup
  • MCMC
  • PyMC3
• Introduction to PyMC3
  • Samplers
  • Basics of PyMC3’s Model()
  • Finding a Person’s Utility Function

Indices and tables

• Index

• Module Index

• Search Page