Existing probabilistic approaches to automated reasoning impose severe restrictions on its knowledge representation scheme. Mainly, this is to ensure that there exists an eeective inferencing algorithm. Unfortunately , this makes the application of these approaches to general domains quite diicult. In this paper, we present a new model called Bayesian multi-networks which uses a rule-based orga...

If the selection of an adequate prior was the major conceptual and modeling challenge of Bayesian analysis, the major implementational challenge is computation. As soon as the model deviates from the conjugate structure, finding the posterior (first the marginal) distribution and the Bayes rule is all but simple. A closed form solution is more an exception than the rule, and even for such close...

This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model siz...

The application of expected utility theory to construct adaptive agents is both computationally intractable and statistically questionable. To overcome these difficulties, agents need the ability to delay the choice of the optimal policy to a later stage when they have learned more about the environment. How should agents do this optimally? An information-theoretic answer to this question is gi...

This chapter discusses a multimodal biometric sensor fusion approach for controlling building access. A Bayesian framework is implemented fusing the decisions received from multiple biometric sensors and achieving the desired system accuracy. The optimal rule is a function of the error cost and a priori probability of an intruder. The chapter presents and then analyzes a Bayesian framework for ...

A nonparametric kernel-based method for realizing Bayes’ rule is proposed, based on kernel representations of probabilities in reproducing kernel Hilbert spaces. The prior and conditional probabilities are expressed as empirical kernel mean and covariance operators, respectively, and the kernel mean of the posterior distribution is computed in the form of a weighted sample. The kernel Bayes’ ru...

In a probability-based reasoning system, Bayes’ theorem and its variations are often used to revise the system’s beliefs. However, if the explicit conditions and the implicit conditions of probability assignments are properly distinguished, it follows that Bayes’ theorem is not a generally applicable revision rule. Upon properly distinguishing belief revision from belief updating, we see that J...

In standard models of Bayesian learning agents reduce their uncertainty about an event’s true probability because their consistent estimator concentrates almost surely around this probability’s true value as the number of observations becomes large. This paper takes the empirically observed violations of Savage’s (1954) sure thing principle seriously and asks whether Bayesian learners with ambi...