Foundations of Bayesian Theory

This paper states necessary and sufficient conditions for the existence, uniqueness, and updating according to Bayes?rule, of subjective probabilities representing individuals?beliefs. The approach is preference based, and the result is an axiomatic subjective expected utility model of Bayesian decision making under uncertainty with statedependent preferences. The theory provides foundations for the existence of prior probabilities representing decision makers?beliefs about the likely realization of events and for the updating of these probabilities according to Bayes?rule.

An Axiomatic Model of Non-Bayesian Updating

This paper models an agent in a three-period setting who does not update according to Bayes'Rule, and who is self-aware and anticipates her updating behavior when formulating plans. The agent is rational in the sense that her dynamic behavior is derived from a single stable preference order on a domain of state-contingent menus of acts. A representation theorem generalizes the (dynamic version of) Anscombe-Aumann's theorem so that both the prior and the way in which it is updated are subjective.Bayes' Rule, non-Bayesian updating, asset price volatility, no-trade theorems, agreeing to bet, common knowledge, temptation, self-control, conservatism, representativeness, overconfidence

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

An Axiomatic Model of Non-Bayesian Updating

This paper models an agent in a three-period setting who does not update according to Bayes'Rule, and who is self-aware and anticipates her updating behavior when formulating plans. The agent is rational in the sense that her dynamic behavior is derived from a single stable preference order on a domain of state-contingent menus of acts. A representation theorem generalizes the (dynamic version of) Anscombe-Aumann's theorem so that both the prior and the way in which it is updated are subjective.Bayes' Rule, non-Bayesian updating, asset price volatility, no-trade theorems, agreeing to bet, common knowledge, temptation, self-control, conservatism, representativeness, overconfidence

On attitude polarization under Bayesian learning with non-additive beliefs

Ample psychological evidence suggests that people’s learning behavior is often prone to a "myside bias" or "irrational belief persistence" in contrast to learning behavior exclusively based on objective data. In the context of Bayesian learning such a bias may result in diverging posterior beliefs and attitude polarization even if agents receive identical information. Such patterns cannot be explained by the standard model of rational Bayesian learning that implies convergent beliefs. As our key contribution, we therefore develop formal models of Bayesian learning with psychological bias as alternatives to rational Bayesian learning. We derive conditions under which beliefs may diverge in the learning process despite the fact that all agents observe the same - arbitrarily large - sample, which is drawn from an "objective" i.i.d. process. Furthermore, one of our learning scenarios results in attitude polarization even in the case of common priors. Key to our approach is the assumption of ambiguous beliefs that are formalized as non-additive probability measures arising in Choquet expected utility theory. As a specific feature of our approach, our models of Bayesian learning with psychological bias reduce to rational Bayesian learning in the absence of ambiguity.Non-additive Probability Measures, Choquet Expected Utility Theory, Bayesian Learning, Bounded Rationality