Results 171 to 180 of about 103,535 (312)

Algebraic formulas for first-passage times of Markov processes in the linear framework. [PDF]

Bull Math Biol
Nam KM, Gunawardena J.
europepmc   +1 more source

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes. [PDF]

J R Soc Interface, 2018
Lin YT, Buchler NE.
europepmc   +1 more source

Insight of Elementary Steps on the Polyethylene and Polypropylene Co‐Pyrolysis

Carbon and Hydrogen, EarlyView.
Pyrolysis of PP initiates first, accelerating PE cracking via secondary radical transfer, leading to n‐hydrocarbon yield enhancement. Py–GC/MS and nanoreactor computation are in agreement on the elementary step proposal. ABSTRACT Pyrolysis with gas chromatography & mass spectrometry (Py–GC/MS) technique was employed to analyze the yield variation of ...
Naiwen Xu, Liang Zou, Na Hua, Chao Xu, Zhenggang Lan, Qi Ou, Bo Peng   +6 more
wiley   +1 more source

PARAMETRIC ESTIMATION OF DIFFUSION PROCESSES SAMPLED AT FIRST EXIT TIME [PDF]

This paper introduces a family of recursively defined estimators of the parameters of a diffusion process. We use ideas of stochastic algorithms for the construction of the estimators.
Jaime A. Londoño
core  

Bounds on Fluctuations of First Passage Times for Counting Observables in Classical and Quantum Markov Processes. [PDF]

J Stat Phys
Bakewell-Smith G, Girotti F, Guţă M, P Garrahan J.   +3 more
europepmc   +1 more source

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes. [PDF]

J Econom, 2016
Li D, Li R.
europepmc   +1 more source

Noncommutative Markov Processes [PDF]

Transactions of the American Mathematical Society, 1966
openaire   +1 more source

A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao, Debaprasad Dutta, Simant R. Upreti   +2 more
wiley   +1 more source

Subexponential lower bounds for <i>f</i>-ergodic Markov processes. [PDF]

Probab Theory Relat Fields
Brešar M, Mijatović A.
europepmc   +1 more source

Efficient maximum likelihood parameterization of continuous-time Markov processes. [PDF]

J Chem Phys, 2015
McGibbon RT, Pande VS.
europepmc   +1 more source