Decision Theory and Bayesian Network
- Introduction to decision analysis
Elements of a decision problem
The role of uncertainty and complexity in decisions
On the necessity of decision theory in management and busienss for leading companies
- Structuring decisions and values
Fundamental and means objective in decision problem
- Getting the decision context right
Introduction to influence diagrams
Influence diagrams and fundamental objective hierarchy
- Modeling decision problem by decision tree and its elements
Influence diagrams and decision tress: relationship
- Cash flows and probabilities in decision tree
- Making choices, Expected Monetary values (EMV) in decision tree
Risk profiles and dominance
- Modeling uncertainty and probabilities
Using
Extended Swanson-Megill
Extended Pearson-Tukey
to assess subjective probabilities as degree of belief
- Using data in CDF empirical modelling
Theoretical distributions and data modeling
Kolmogorov-Smirnov distance (K-S)
Anderson-Darling distance (A-D)
- Utility concept, Utility curve, Expected utility and decision analysis with utility curve
Exponential utility function and ways to determine its parameter
- Introduction to statistical decision making, Bayesian decisions
Bayesian estimators
Minimax estimators
Risk function
- Introduction to statistical decision making, Bayesian decisions
Bayesian estimators
Minimax estimators
Risk function
- Fisher information function and its application
- Uncertainty modeling of relationships by Logit regression
- Application of decision tree modeling in classification and machine learning
- Haunts algorithm for decision tree modeling