Results 31 to 40 of about 1,345 (165)
WASABI: a dynamic iterative framework for gene regulatory network inference
BMC Bioinformatics, 2019 Background Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology.
Arnaud Bonnaffoux +6 more
doaj +1 more source
Piecewise Deterministic Markov Processes in Biological Models [PDF]
, 2014 in: Semigroup of Operators - Theory and Applications, J. Banasiak et al. (eds.), Springer Proceedings in Mathematics & Statistics 113, (2015), pp.
Rudnicki, Ryszard, Tyran-Kaminska, Marta
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Hypocoercivity of piecewise deterministic Markov process-Monte Carlo [PDF]
The Annals of Applied Probability, 2021 In this work, we establish $\mathrm{L}^2$-exponential convergence for a broad class of Piecewise Deterministic Markov Processes recently proposed in the context of Markov Process Monte Carlo methods and covering in particular the Randomized Hamiltonian Monte Carlo, the Zig-Zag process and the Bouncy Particle Sampler. The kernel of the symmetric part of
Andrieu, Christophe +3 more
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Piecewise Deterministic Markov Processes for Bayesian Neural Networks
CoRR, 2023 Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood.
Goan, Ethan +3 more
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Approximations of Piecewise Deterministic Markov Processes and their convergence properties
Stochastic Processes and their Applications, 2022 Piecewise deterministic Markov processes (PDMPs) are a class of stochastic processes with applications in several fields of applied mathematics spanning from mathematical modeling of physical phenomena to computational methods. A PDMP is specified by three characteristic quantities: the deterministic motion, the law of the random event times, and the ...
Andrea Bertazzi +2 more
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Polynomial Convergence Rates of Piecewise Deterministic Markov Processes
Methodology and Computing in Applied Probability, 2022 Abstract We consider piecewise-deterministic Markov processes such as the Bouncy Particle sampler, on target densities with polynomial tails. Using direct drift condition methods, we provide bounds on the polynomial order of the processes' convergence rate to stationary, on both one-dimensional and high-dimensional state spaces, in both total ...
Roberts, Gareth O. +1 more
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Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Impulse Control of Piecewise Deterministic Markov Processes
The Annals of Applied Probability, 1995 An optimal impulse control problem for piecewise deterministic Markov processes is considered. This control problem is converted to an equivalent dynamic control problem. Necessary and sufficient conditions for optimality for the former problem are given in terms of the value function of the latter problem.
Dempster, M. A. H., Ye, J. J.
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Advances in causal discovery methods for ecological time series
Biological Reviews, EarlyView.ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki +6 more
wiley +1 more source
Information Design for Early‐Stage Dose‐Finding Trials
Naval Research Logistics (NRL), EarlyView.ABSTRACT To enhance enrollment rates in early‐stage dose‐finding clinical trials, we propose an information design approach, where the clinical investigator (CI) commits to an information releasing mechanism (IRM) based on the treatment's uncertain efficacy and toxicity to encourage patients to participate in the trial.
Amin Khademi, Ningyuan Chen
wiley +1 more source