Dempster–Shafer fusion of evidential pairwise Markov fields

Pairwise Markov Chains and Bayesian Unsupervised Fusion

Pairwise Markov random fields and segmentation

The use of random elds, which allows one to take into account the spatial interaction among random variables in complex systems, becomes a frequent tool in numerous problems of statistical mechanics, spatial statistics, neural network modelling, and others. In particular, Markov random eld based techniques can be of exceptional eeciency in some image processing problems, like segmen-tation or e...

Parameter Estimation in Pairwise Markov Fields

Hidden Markov fields (HMF), which are widely applied in various problems arising in image processing, have recently been generalized to Pairwise Markov Fields (PMF). Although the hidden process is no longer necessarily a Markov one in PMF models, they still allow one to recover it from observed data. We propose in this paper two original methods of parameter estimation in PMF, based on general ...

Evidential Markov Decision Processes

This paper proposes a new model, the EMDP (Evidential Markov Decision Process). It is a MDP (Markov Decision Process) for belief functions in which rewards are defined for each state transition, like in a classical MDP, whereas the transitions are modeled as in an EMC (Evidential Markov Chain), i.e. they are sets transitions instead of states transitions. The EMDP can fit to more applications t...

An Evidential Measure of Risk in Evidential Markov Chains

This paper provides a new method for computing a risk criterion for decision-making in systems modelled by an Evidential Markov Chain (EMC), which is a generalization to the Dempster-Shafer’s Theory of Evidence [1]: it is a Markov chain manipulating sets of states instead of the states themselves. A cost is associated to each state. An evidential risk measurement derived from the statistical on...