piecewise-deterministic Markov process
Piecewise deterministic Markov process — recent results
... Beyond the problems of existence, uniqueness, and regularity of an invariant probability measure, an impor- tant and challenging question is to get explicit and efficient rates of convergence to equilibrium for PDMPs. It ...
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Piecewise Deterministic Markov Processes for Continuous Time Monte Carlo
... a piecewise deterministic Markov ...to piecewise deterministic Markov processes, covering the aspects relevant to these new Monte Carlo algorithms, with a view to making the ...
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A piecewise deterministic scaling limit of Lifted Metropolis Hastings in the Curie Weiss model
... elementary Markov process which has so far received only very limited attention in the ...limit process is a one-dimensional piecewise deterministic Markov process which ...
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Model checking of continuous time Markov Chains against timed automata specifications
... continuous-time Markov chain ( CTMC ) C against a linear real-time specification given as a deterministic timed automaton ( DTA ) A with finite or Muller acceptance ...finite piecewise ...
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Piecewise deterministic Markov processes for continuous time Monte Carlo
... a piecewise deterministic Markov ...to piecewise deter- ministic Markov processes, covering the aspects relevant to these new Monte Carlo algorithms, with a view to making the ...
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Numerical methods for piecewise deterministic Markov processes with boundary
... Abstract. In this paper is described the general aspect of a numerical method for piecewise de- terministic Markov processes with boundary. Under very natural hypotheses, a crucial result about uniqueness ...
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Nonequivalence of Controllability Properties for Piecewise Linear Markov Switch Processes *
... of piecewise deterministic Markov processes of switch ...uncontrolled Markov process corresponding to spikes inducing regime ...
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Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains
... Note that this gain in computational efficiency does not come for free, as it follows from Jensen’s inequality that the overall rate of events will be higher. This makes mixing of the PDMC process slower. It is also ...
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Model based Testing for Software Systems: An Application of Markov Modulated Markov Process
... using Markov chains to create system behavioral models from available system usage ...the Markov Modulated Markov Process to model hidden ...
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Process plan controllers for non deterministic manufacturing systems
... [8], process recipes may contain (bounded) cy- cles, and we assume that cycles are removed by unfold- ing the recipe up to the bound in a pre-processing ...(finite) process of manufacturing a given product; ...
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Markov denumerable process and queue theory
... queue process Q ∗ , which is the process with the restriction of the state-dependent ...queue process back to our initial queueing model, that is to find the expression of ...
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Multi-Objective Markov Decision Processes for Data-Driven Decision Support
... One can view the NDP as a compact way of expressing a set of policies that might be executed. Suppose that #A “ |Πpsq|, the number of actions provided by the NDP Π, is the same at all states. Then the number of policies ...
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Induction of Greedy Controllers for Deterministic Treebank Parsers
... defined Markov parsing models, described how to estimate pa- rameters for them, and showed how the deter- ministic parsers we implemented are greedy ver- sions of MPM ...
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Aggregation of Markov flows I : theory
... the Markov case, this can not be done with the above schemes because the dynamics of an N -state Markov chain has N − 1 eigenvalues (counting multiplicity) so reduction of N must lose some of the ...
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Pulsatile electrical stimulation of auditory nerve fibres : a modelling approach
... The stationary Markov model cannot fit the whole data. But it can almost accurately fit the gradient data. When constructing the PST histograms by adding the non-autonomous factor , the firing probability quickly ...
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Generic Reinforcement Learning Beyond Small MDPs
... Deterministic TMaze In the case of corridor length 50, the optimal policy has a value of -0.018. The agent reaches the optimal policy in every run once the ε -exploration has been turned off at 400 epochs. See ...
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Dirichlet Process Mixture Models For Markov Processes
... A Markov process is one of the most natural extensions of the ...a Markov process, one assumes that given the present, the future will not depend on the past any ...Markov ...
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Large scale dynamics and fluctuations in non equilibrium stochastic particle systems
... In Chapter 5, we study lower current large deviations for general TAZRP with concave flux functions J (ρ), which can be realized by phase separated density profiles. Travelling wave profiles related to non-entropic ...
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Cost-effectiveness of colorectal cancer screening in Ukraine
... a deterministic Markov cohort model to assess the cost-effectiveness of three colorectal cancer screening strategies [fecal occult blood test (FOBT) every year, flexible sigmoidoscopy with FOBT every 5 ...
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Reachability-based model reduction for Markov decision process
... One of the biggest challenges in the probabilistic plan- ning is to solve large Markov decision processes (MDPs) [1]. This is because the number of states in an MDP grows exponentially with the number of state ...
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