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Chapter One INTRODUCTION AND PREVIEW
On Formulating Decision Trees
18 other sections not shown
after-four assess assume assumption basic canonical randomization Bayesian calculate canonical lottery canonical probability cardinally admissible cardinally dominated certainty equivalent Chapter choice choose concave conditional probabilities consequences consists cumulative distribution function decision analysis decision problem decision tree Definition denote determine dpuu dpuu's ee's elementary events Equation Exercise Figure finite number g-move graph hence implies inference infinite judgments likelihood function Likelihood Principle mass function maximin maximizes Method monetary returns move mutually exclusive n-tuple Normal density function normal form notation Note Observation obtain opportunity optimal strategy ordinally outcomes P(ft parameters posterior probability preferences prior probability Proof pure strategies quantify randomized strategy real number real random variable real-valued risk aversion sample sampling-theoretic satisfy Section 7.3 sharing rules Show Si's simulation SL'S stage standard extensive form step stochastic strictly increasing subset sufficient statistic Suppose Theorem U(PS utility characteristic utility function verify