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Does the strength of a particular belief depend upon the significance we attach to it? Do we move from one context to another, remaining in the same doxastic state concerning p, yet holding a stronger...
We generalize recent theoretical work on the minimal number of layers of narrow deep belief networks that can approximate any probability distribution on the states of their visible units arbitrarily ...
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-pass...
There are at least two ways to interpret numerical degrees of belief in terms of betting: 1. You can offer to bet at the odds defined by the degrees of belief. 2. You can make the judgement that a ...
There is an ongoing concern among managers and scholars: how can firms develop trust and achieve performance? Our paper aims to review the emerging perspective of trust and propose mechanisms to build...
Bayesian belief networks provide estimates of conditional probabilities, called query responses. The precision of these estimates is assessed using posterior distributions. This paper discusses two ...
Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimat...

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