Results 71 to 80 of about 1,363 (183)
Minimizing risk probability for infinite discounted piecewise deterministic Markov decision processes [PDF]
summary:The purpose of this paper is to study the risk probability problem for infinite horizon piecewise deterministic Markov decision processes (PDMDPs) with varying discount factors and unbounded transition rates.
Wen, Xian, Huo, Haifeng, Cui, Jinhua
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Cores for Piecewise-Deterministic Markov Processes used in Markov Chain Monte Carlo
, 2022 We show fundamental properties of the Markov semigroup of recently proposed MCMC algorithms based on Piecewise-deterministic Markov processes (PDMPs) such as the Bouncy Particle Sampler, the Zig-Zag process or the Randomized Hamiltonian Monte Carlo ...
Holderrieth, Peter
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Journal of Time Series Analysis, Volume 47, Issue 3, Page 727-748, May 2026.ABSTRACT We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM‐type statistics attain the minimax optimal rates for localizing the change point.
Annika Hüselitz, Housen Li, Axel Munk
wiley +1 more source
Stability conditions for a Piecewise Deterministic Markov Process [PDF]
In the present paper we study the stability of a threshold continuos-time model that belongs to the class of Piecewise Deterministic Markov Processes. We derive a sufficient condition on the coefficients of the model to ensure the exponential ergodicity ...
Fonseca Giovanni
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Eyring-Kramers type formulas for some piecewise deterministic Markov processes
, 2023 In this work, we give sharp asymptotic equivalents in the small temperature regime of the smallest eigenvalues of the generator of some piecewise deterministic Markov processes (including the ZigZag process and the Bouncy Particle Sampler process) with ...
Le Peutrec, Dorian +2 more
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Causal Inference for Geostatistical Data Using an INLA‐based Spatial Propensity Score
Environmetrics, Volume 37, Issue 3, April 2026.ABSTRACT In this paper, we propose a Bayesian approach for spatial causal inference based on combining spatial propensity scoring with Integrated Nested Laplace Approximation. The method models both local and spillover exposure effects via multiple likelihoods and treats counterfactuals as missing data, allowing inference also for non‐Gaussian outcomes.
Chiara Di Maria +3 more
wiley +1 more source
Hybrid Reaction–Diffusion Epidemic Models: Dynamics and Emergence of Oscillations
Mathematical Methods in the Applied Sciences, Volume 49, Issue 5, Page 4074-4095, 30 March 2026.ABSTRACT In this paper, we construct a hybrid epidemic mathematical model based on a reaction–diffusion system of the SIR (susceptible‐infected‐recovered) type. This model integrates the impact of random factors on the transmission rate of infectious diseases, represented by a probabilistic process acting at discrete time steps.
Asmae Tajani +2 more
wiley +1 more source
Optimal Resource Allocation in Relay-Assisted ISAC Systems via Deep Reinforcement Learning
IEEE Open Journal of the Communications SocietyThis article studies joint beamforming and power allocation in a full-duplex decode-and-forward (FD-DF) relay-assisted monostatic integrated sensing and communication (ISAC) system. The base station performs monostatic sensing in the presence of clutter,
Hamidreza Hojjati +2 more
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Optimal dividends for a NatCat insurer in the presence of a climate tipping point
Canadian Journal of Statistics, Volume 54, Issue 1, March 2026.Abstract We study optimal dividend strategies for an insurance company facing natural catastrophe claims, anticipating the arrival of a climate tipping point after which the claim intensity and/or the claim size distribution of the underlying risks deteriorates irreversibly.
Hansjörg Albrecher +2 more
wiley +1 more source
Mean first passage times for piecewise deterministic Markov processes and the effects of critical points [PDF]
, 2017 In this paper, we use probabilistic methods to determine the mean first passage time (MFPT) for a two-state piecewise deterministic Markov process (PDMP), also known as a dichotomous noise process, to escape from a finite interval.
Sean D Lawley +3 more
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