This paper compares Bayesian decision theory with robust decision theory where the decision maker optimizes with respect to the worst state realization. For a class of robust decision problems there exists a sequence of Bayesian decision problems whose solution converges towards the robust solution. It is shown that the limiting Bayesian problem displays infinite risk aversion and that decision...

the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to enginee...

Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented i...

one of the methods used in the analysis of data related to diseases, and their underlying reasons is drawing geographical map. mapping diseases is a valuable tool to determine the regions of high rate of infliction requiring therapeutic interventions. the objective of this study was to investigate obesity pattern in iran by drawing geographical maps based on bayesian spatial model to recognize ...

summary This study provides a model-based approach for implementing retail category management with a focus on markup decisions. We develop a reduced form Bayesian econometric model that captures price and sales variation for a retail chain. Based on this model, we derive the optimal retail markups on individual brands within a product category by maximizing the total category profit over a fin...

In this paper we investigate whether a combination of topic specific language models can outperform a general purpose language model, using a trigram model as our baseline model. We show that in the ideal case — in which it is known beforehand which model to use — specific models perform considerably better than the baseline model. We test two methods that combine specific models and show that ...

As a compact representation of joint probability distributions over dependence graph random variables, and tool for modelling reasoning in the presence uncertainty, Bayesian networks are great importance artificial intelligence to combine domain knowledge, capture causal relationships, or learn from incomplete datasets. Known as NP-hard problem classical setting, inference pops up class algorit...

Drought is a feature of climate that can occur in virtually all climates. Therefore, it is aninevitable global but site-specific phenomenon which requires tools to predict and strategies andoptions to cope with it. In this research, the ability and effectiveness of the Bayesian DecisionNetworks (BDNs) approach in decision-making and evaluating drought management options forrainfed wheat product...

Motivated reasoning occurs when judgements subserve motives that go beyond accuracy seeking. Substantial evidence indicates motivated political is ubiquitous. This hard to reconcile with computational theories (following Marr's terminology, describing the fundamental principles underlying a cognitive process) like Bayesian inference, because these rely on maximization. Hence, often interpreted ...