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Identification of Systems With Regime Switching and Unmodeled Dynamics

G. George Yin, Shaobai Kan, Le Yi Wang, Cheng-Zhong Xu
2009 IEEE Transactions on Automatic Control  
An adaptive algorithm with variable step sizes is introduced for tracking the time-varying parameters. Convergence and error bounds are derived.  ...  In the first class, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains with transition rates sufficiently faster than adaptation rates of the identification  ...  Thus we can construct algorithms that tracks the Markov chain , adaptively estimates the step size, and estimates the mean square derivative simultaneously.  ... 
doi:10.1109/tac.2008.2009487 fatcat:afa4gilugvfajg6dal3d3qxhwy

Dynamic adaptive partitioning for nonlinear time series

P Buhlmann
1999 Biometrika  
We make it constructive by an approach based on quantisation of the data and adaptively modelling partition cells with a parsimonious Markov chain.  ...  It is a new extension of finite-valued variable length Markov chains to processes with values in Rd.  ...  With a quantised variable length Markov chain, the partitions are given through all p lagged variables, where p is the order Dynamic adaptive partitioning of the variable length Markov chain, and constants  ... 
doi:10.1093/biomet/86.3.555 fatcat:knnituclabh7ricxikgn3e62lm

Reliability Analysis Based on Optimization Random Forest Model and MCMC

Fan Yang, Jianwei Ren
2020 CMES - Computer Modeling in Engineering & Sciences  
Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed.  ...  Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy.  ...  Adaptive Sampling Based on Markov Chain In this section, samples in important region around the limit state are generated by Markov Chain Monet Carlo simulation.  ... 
doi:10.32604/cmes.2020.08889 fatcat:ulrqdoglcjboxe5rint7mrcv7e

Anti-Inflammatory Effects of Resveratrol, Curcumin and Simvastatin in Acute Small Intestinal Inflammation

Stefan Bereswill, Melba Muñoz, André Fischer, Rita Plickert, Lea-Maxie Haag, Bettina Otto, Anja A. Kühl, Christoph Loddenkemper, Ulf B. Göbel, Markus M. Heimesaat, Niyaz Ahmed
2010 PLoS ONE  
We further improve the Splash sampler through adaptive tree construction.  ...  Gibbs chains.  ...  Acknowledgments We thank Yee Whye Teh and Maneesh Sahani for helpful discussions.  ... 
doi:10.1371/journal.pone.0015099 pmid:21151942 pmcid:PMC2997083 fatcat:poeuvcg7x5gb7or2iscg7v2gp4

Markov approximation and consistent estimation of unbounded probabilistic suffix trees

Denise Duarte, Antonio Galves, Nancy L. Garcia
2006 Bulletin of the Brazilian Mathematical Society  
Our basic tool is the canonical Markov approximation which enables to approximate the chain of infinite order by a sequence of variable length Markov chains of increasing order.  ...  We consider infinite order chains whose transition probabilities depend on a finite suffix of the past. These suffixes are of variable length and the set of the lengths of all suffix is unbounded.  ...  This paper is part of PRONEX/FAPESP's Project Stochastic behavior, critical phenomena and rhythmic pattern identification in natural languages (grant number 03/09930-9) and CNPq's project Stochastic modeling  ... 
doi:10.1007/s00574-006-0029-7 fatcat:6qr6dsuxbzcdxiax3newvpkdky

An Adaptively Constructed Algebraic Multigrid Preconditioner for Irreducible Markov Chains [article]

James Brannick, Karsten Kahl, Sonja Sokolovic
2014 arXiv   pre-print
The computation of stationary distributions of Markov chains is an important task in the simulation of stochastic models.  ...  We demonstrate its fast convergence and the favorable scaling behavior for various test problems.  ...  The size of the resulting Markov chain depends on the number of tokens in the initial marking and grows very fast.  ... 
arXiv:1402.4005v1 fatcat:cfy4txw36fb7nk4rb5ikyarxlu

Sequential Monte Carlo on large binary sampling spaces

Christian Schäfer, Nicolas Chopin
2011 Statistics and computing  
chain exploration by orders of magnitude.  ...  A practical motivation for this problem is variable selection in a linear regression context.  ...  We acknowledge the StatLib data archive and the UCI Machine Learning Repository for providing the data sets used in this work.  ... 
doi:10.1007/s11222-011-9299-z fatcat:nzugihsi4ngnpo4uonyv7ipi4y

Sequential Monte Carlo on large binary sampling spaces [article]

Christian Schäfer
2011 arXiv   pre-print
chain exploration.  ...  A practical motivation for this problem is variable selection in a linear regression context.  ...  We would like to thank Pierre Jacob and two anonymous referees for their valuable comments on this paper.  ... 
arXiv:1101.6037v4 fatcat:4r2xjv2yvfbrxox5okp42ki2ce

MCMC algorithms for Subset Simulation

Iason Papaioannou, Wolfgang Betz, Kilian Zwirglmaier, Daniel Straub
2015 Probabilistic Engineering Mechanics  
The method requires sampling from conditional distributions, which is achieved through Markov Chain Monte Carlo (MCMC) algorithms.  ...  Subset Simulation is an adaptive simulation method that efficiently solves structural reliability problems with many random variables.  ...  chain follow the target distribution by construction.  ... 
doi:10.1016/j.probengmech.2015.06.006 fatcat:5dtzx3nzsffx7mm5pwnlyw3hzi

When does memory speed-up mixing?

Simon Apers, Alain Sarlette, Francesco Ticozzi
2017 2017 IEEE 56th Annual Conference on Decision and Control (CDC)  
We investigate under which conditions a higherorder Markov chain, or more generally a Markov chain on an extended state space, can mix faster than a standard Markov chain on a graph of interest.  ...  We find that, depending on the constraints on the dynamics, two very different scenarios can emerge: under strict invariance of the target marginal and for general initialization of the lifted chain no  ...  Theorem 2: Under constraints (SI) a lifted Markov chain can be constructed, such that τ M (1/4) < D G + 1, with D G the graph diameter; the associated lifted graph has of order D G N 2 nodes.  ... 
doi:10.1109/cdc.2017.8264390 dblp:conf/cdc/ApersST17 fatcat:urtd7bt67bf35mfhr6kqkmqtbe

SABRE: A Tool for Stochastic Analysis of Biochemical Reaction Networks [article]

Frederic Didier, Thomas A. Henzinger, Maria Mateescu, Verena Wolf
2010 arXiv   pre-print
Biochemical reactions networks represent biological systems studied at a molecular level and these reactions can be modeled as transitions of a Markov chain.  ...  SABRE implements fast adaptive uniformization (FAU), a direct numerical approximation algorithm for computing transient solutions of biochemical reaction networks.  ...  ACKNOWLEDGMENT We thank Marius Mateescu for valuable advices on the web interface and Nick Barton for an introduction to population genetics.  ... 
arXiv:1005.2819v1 fatcat:zlrszdk33jcfrf732s732j4pzm

Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library [article]

Amir Shahmoradi, Fatemeh Bagheri, Joshua Alexander Osborne
2020 arXiv   pre-print
ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, in  ...  In addition to providing access to fast high-performance serial/parallel Monte Carlo and MCMC sampling routines, the ParaMonte::Python library provides extensive post-processing and visualization tools  ...  Acknowledgements We acknowledge the use of supercomputing resources at Texas Advanced Computing Center for the development and testing of ParaMonte::Python library.  ... 
arXiv:2010.00724v1 fatcat:lqwvequztbcp3dduew4julri2q

Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Artūrs Stepčenko, Jurijs Čižovs
2016 Information Technology and Management Science  
In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order.  ...  A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures.  ...  It helps make Markov chain adaptive.  ... 
doi:10.1515/itms-2016-0009 fatcat:rbao5xxntjejbhkyikcw7dvvnm

Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms [article]

James W. Myers, Kathryn Blackmond Laskey, Tod S. Levitt
2013 arXiv   pre-print
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data.  ...  Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.  ...  The convergence curve is a measure of how fast a Markov chain or population of Markov chains converges to the stationary distribution.  ... 
arXiv:1301.6726v1 fatcat:ze5u6apl2jcbtfvqht4dve55tu

How does a stochastic optimization/approximation algorithm adapt to a randomly evolving optimum/root with jump Markov sample paths

G. Yin, C. Ion, V. Krishnamurthy
2007 Mathematical programming  
Our analysis assumes that the noisy 2 Yin, Ion, Krishnamurthy observations contain a (nonsmooth) jump process modeled by a discretetime Markov chain whose transition frequency varies much faster than the  ...  adaptation rate of the stochastic optimization algorithm.  ...  In comparison, the Markov chain is a fast process -it varies at an order of magnitude faster than the adaptation rate of the stochastic approximation algorithm.  ... 
doi:10.1007/s10107-007-0145-1 fatcat:6eqrggbeqrannlaifxxnox6aga
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