in this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. then a common general forward modeling for them is given and the corresponding inversion problem is presented. then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a baye...

In this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. Then a common general forward modeling for them is given and the corresponding inversion problem is presented. Then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a Baye...

Large survey data are often accompanied by sampling weights that reflect the inequality probabilities for selecting samples in complex sampling. Sampling weights act as an expansion factor that, by scaling the subjects, turns the sample into a representative of the community. The quasi-maximum likelihood method is one of the approaches for considering sampling weights in the frequentist framewo...

This paper investigates the relations between Timmer et al.’s proposal for explaining Bayesian networks with structured argumentation and abstract models of probabilistic argumentation. First some challenges are identified for incorporating probabilistic notions of argument strength in structured models of argumentation. Then it is investigated to what extent Timmer et al’s approach meets these...

This paper investigates the relations between Timmer et al.’s proposal for explaining Bayesian networks with structured argumentation and abstract models of probabilistic argumentation. First some challenges are identified for incorporating probabilistic notions of argument strength in structured models of argumentation. Then it is investigated to what extent Timmer et al’s approach meets these...

We present a new framework for combining logic with probability, and demonstrate the application of this framework to breast cancer prognosis. Background knowledge concerning breast cancer prognosis is represented using logical arguments. This background knowledge and a database are used to build a Bayesian net that captures the probabilistic relationships amongst the variables. Causal hypothes...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

In risk analysis based on Bayesian framework, premium calculation requires&nbsp;specification of a prior distribution for the risk parameter in the heterogeneous portfolio. When the prior knowledge is vague, the E-Bayesian and robust Bayesian analysis&nbsp;can be used to handle the uncertainty in specifying the prior distribution by considering a class of priors instead of a single prior. In th...