In this paper we contribute two novel methods that simplify the demands of knowledge elicitation for particular types of Bayesian networks. The first method simplifies the task of experts providing conditional probabilities when the states that a random variable takes can be described by a fully ordered set. In this order, each state’s definition is inclusive of the preceding state’s definition...

The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship betwee...

In a companion article in this issue, parameter estimation using exponential models was addressed when the form of the model is known (i.e., when the number of exponentials and whether a constant offset is present are known). In this article, we apply Bayesian probability theory to the problem of determining the functional form of the model. The calculations are implemented using Markov chain M...

Probability has played a central role in models of perception for more than a century, but a look at probabilistic concepts in the literature raises many questions. Is being Bayesian the same as being optimal? Are recent Bayesian models fundamentally different from classic signal detection theory models? Do findings of near-optimal inference provide evidence that neurons compute with probabilit...

The process by which the human visual system parses an image into contours, surfaces, and objects--perceptual grouping--has proven difficult to capture in a rigorous and general theory. A natural candidate for such a theory is Bayesian probability theory, which provides optimal interpretations of data under conditions of uncertainty. But the fit of Bayesian theory to human grouping judgments ha...

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Probability theory has been traditionally used to characterize both types of uncertainty. E.g. aleatory uncertainty can be best dealt with using the frequentist approach associated with traditional probability theory. However, probability theory is not capable of completely capturing epistemic uncertainty. The traditional method was called Bayesian Probability. In this method, it is necessary t...

Orthodox Bayesian decision theory requires an agent’s beliefs representable by a real-valued function, ideally a probability function. Many theorists have argued this is too restrictive; it can be perfectly reasonable to have indeterminate degrees of belief. So doxastic states are ideally representable by a set of probability functions. One consequence of this is that the expected value of a ga...

Data modeled as sums of exponentials arise in many areas of science and are common in NMR. However, exponential parameter estimation is fundamentally a difficult problem. In this article, Bayesian probability theory is used to obtain optimal exponential parameter estimates. The calculations are implemented using Markov chain Monte Carlo with simulated annealing to draw samples from the joint po...