In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast medium with biological meaningful kinetic parameters. As simple models often fail to adequately describe the observed uptake behavior, more complex compartment models have been proposed. However, the nonlinear regression problem arising from ...

We propose a method for simultaneous recovery of depth and restoration of scene intensity, given two defocused images of a scene. The space-variant blur parameter and the focused image of the scene are modeled as Markov random.fields (MRFs). Line fields are included to preserve discontinuities. The joint posterior distribution of the blur parameter and the intensity process is examined for loca...

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we pro...

This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann Machines. We provide an illustrative example c...

We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largenes...

In their 2008 and 2009 articles, Sumner and colleagues introduced the "squangles"-a small set of Markov invariants for phylogenetic quartets. The squangles are consistent with the general Markov (GM) model and can be used to infer quartets without the need to explicitly estimate all parameters. As the GM model is inhomogeneous and hence nonstationary, the squangles are expected to perform well ...

Markov Logic Networks (MLNs) are a prominent statistical relational model that have been proposed as a unifying framework for statistical relational learning. As part of this unification, their authors proposed methods for converting other statistical relational learners into MLNs. For converting a first order Bayes net into an MLN, it was suggested to moralize the Bayes net to obtain the struc...

This paper presents a modified hidden Markov model (HMM) filtering algorithm for detecting multiple dim targets in image sequence under low SNR condition. The proposed algorithm consists of three steps. As a first step, morphological filtering is applied for extracting features in pre-processing level. The second step is a hidden Markov model filter. To enhance a detecting performance of the fi...

Model transfer refers to the process of transferring information from a model that was previously identified for one task (source) to a new task (target). For decision tasks in unknown Markov environments, we profit through model transfer by using information from related tasks, e.g. transition knowledge and solution (policy) knowledge, to quickly determine an appropriate model of the new task ...

In parametric Markov Decision Processes (PMDPs), transition probabilities are not fixed, but are given as functions over a set of parameters. A PMDP denotes a family of concrete MDPs. This paper studies the synthesis problem for PCTL in PMDPs: Given a specification Φ in PCTL, we synthesise the parameter valuations under which Φ is true. First, we divide the possible parameter space into hyper-r...