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APPROXIMATING PROBABILISTIC INFERENCE IN BAYESIAN BELIEF NETWORKS

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1993年第3期15卷 246-255页

作者： DAGUM, P CHAVEZ, RM ROCKWELL INT CORP PALO ALTO LABPALO ALTOCA

A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless P=NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks. stochastic simulation methods, which often improve run times, provide an alternative to exact inference algorithms. We present such a stochastic simulation algorithm D-BNRAS that is a randomized approximation scheme. To analyze the run time, we parameterize belief networks by the dependence value D(xi), which is a measure of the cumulative strengths of the belief network dependencies given background evidence xi. This parameterization defines the class of f-dependence networks. The run time of D-BNRAS is polynomial when f is a polynomial function. Thus, the results of this paper prove the existence of a class of belief networks for which inference approximation is polynomial and, hence, provably faster than any exact algorithm.

关键词： Intelligent networks Bayesian methods Inference algorithms stochastic processes Approximation algorithms Computational modeling Polynomials Medical simulation Knowledge representation Sampling methods INSPEC: Controlled Indexing uncertainty handling Bayes methods belief maintenance inference mechanisms polynomials probabilistic logic INSPEC: Non-Controlled Indexing polynomial probabilistic inference approximation reasoning Bayesian belief networks conditional probabilities stochastic simulation algorithm D-BNRAS

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stochastic approaches for modelling in vivo reactions

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2004年第3期28卷 165-178页

作者： Turner, TE Schnell, S Burrage, K Oxford Ctr Ind & Appl Math Math Inst Oxford OX1 3LB England Ctr Math Biol Math Inst Oxford OX1 3LB England Univ Oxford Christ Church Oxford OX1 1DP England Univ Queensland Dept Math Adv Comp Modelling Ctr Brisbane Qld 4072 Australia

In recent years, stochastic modelling has emerged as a physically more realistic alternative for modelling in vivo reactions. There are numerous stochastic approaches available in the literature;most of these assume that observed random fluctuations are a consequence of the small number of reacting molecules. We review some important developments of the stochastic approach and consider its suitability for modelling intracellular reactions. We then describe recent efforts to include the fluctuation effects caused by the structural organisation of the cytoplasm and the limited diffusion of molecules due to macromolecular crowding. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： intracellular reactions stochastic simulation algorithm tau-leap method quasi-steady-state approximation fractal-like kinetics

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stochastic optimization-based study of dimerization kinetics

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JOURNAL OF CHEMICAL SCIENCES 2013年第6期125卷 1619-1627页

作者： Talukder, Srijeeta Sen, Shrabani Metzler, Ralf Banik, Suman K. Chaudhury, Pinaki Univ Calcutta Dept Chem Kolkata 700009 India Univ Potsdam Inst Phys & Astron D-14476 Potsdam Germany Tampere Univ Technol Dept Phys FI-33101 Tampere Finland Bose Inst Dept Chem Kolkata 700009 India

We investigate the potential of numerical algorithms to decipher the kinetic parameters involved in multi-step chemical reactions. To this end, we study dimerization kinetics of protein as a model system. We follow the dimerization kinetics using a stochastic simulation algorithm and combine it with three different optimization techniques (genetic algorithm, simulated annealing and parallel tempering) to obtain the rate constants involved in each reaction step. We find good convergence of the numerical scheme to the rate constants of the process. We also perform a sensitivity test on the reaction kinetic parameters to see the relative effects of the parameters for the associated profile of the monomer/dimer distribution.

关键词： stochastic optimization dimerization kinetics sensitivity analysis stochastic simulation algorithm probability distribution function

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Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks

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COMPUTERS & CHEMICAL ENGINEERING 2016年 90卷 111-120页

作者： Jang, Hong Kim, Kwang-Ki K. Braatz, Richard D. Gopaluni, R. Bhushan Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn 291 Daehak Ro Taejon 305701 South Korea Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA MIT Dept Chem Engn Cambridge MA 02139 USA Univ British Columbia Dept Chem & Biol Engn 2360 East Mall Vancouver BC V6T 1Z3 Canada

A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Sparse parameter estimation Regularized maximum likelihood estimation Mono-molecular biochemical reaction network Chemical master equation stochastic simulation algorithm

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Computationally-efficient stochastic cluster dynamics method for modeling damage accumulation in irradiated materials

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JOURNAL OF COMPUTATIONAL PHYSICS 2015年 300卷 254-268页

作者： Hoang, Tuan L. Marian, Jaime Bulatov, Vasily V. Hosemann, Peter Univ Calif Berkeley Dept Nucl Engn Berkeley CA 94720 USA Lawrence Livermore Natl Lab Phys & Life Sci Directorate Livermore CA 94550 USA Univ Calif Los Angeles Dept Mat Sci & Engn Los Angeles CA 94720 USA

An improved version of a recently developed stochastic cluster dynamics (SCD) method (Marian and Bulatov, 2012) [6] is introduced as an alternative to rate theory (RT) methods for solving coupled ordinary differential equation (ODE) systems for irradiation damage simulations. SCD circumvents by design the curse of dimensionality of the variable space that renders traditional ODE-based RT approaches inefficient when handling complex defect population comprised of multiple (more than two) defect species. Several improvements introduced here enable efficient and accurate simulations of irradiated materials up to realistic (high) damage doses characteristic of next-generation nuclear systems. The first improvement is a procedure for efficiently updating the defect reaction-network and event selection in the context of a dynamically expanding reaction-network. Next is a novel implementation of the tau-leaping method that speeds up SCD simulations by advancing the state of the reaction network in large time increments when appropriate. Lastly, a volume rescaling procedure is introduced to control the computational complexity of the expanding reaction-network through occasional reductions of the defect population while maintaining accurate statistics. The enhanced SCD method is then applied to model defect cluster accumulation in iron thin films subjected to triple ion-beam (Fe3+, He+ and H+) irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo simulations are prohibitively expensive. (C) 2015 Elsevier Inc. All rights reserved.

关键词： stochastic cluster dynamics Irradiation damage stochastic simulation algorithm Tau-leaping

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Tensor product algorithms for inference of contact network from epidemiological data

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Dolgov, Sergey Savostyanov, Dmitry Univ Bath Bath BA2 7AY England Univ Essex Wivenhoe Pk Colchester CO4 3SQ England

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

关键词： Epidemiological modelling Networks Tensor train stochastic simulation algorithm Markov chain Monte Carlo Bayesian inference

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Exact stochastic simulations of intra-cellular transport by mechanically coupled molecular motors

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JOURNAL OF COMPUTATIONAL SCIENCE 2011年第4期2卷 324-334页

作者： Helmuth, Jo A. Reboux, Sylvain Sbalzarini, Ivo F. ETH Inst Theoret Comp Sci MOSAIC Grp CH-8092 Zurich Switzerland ETH Swiss Inst Bioinformat CH-8092 Zurich Switzerland

Numerous processes in live cells depend on active, motor-driven transport of cargo and organelles along the filaments of the cytoskeleton. Understanding the resulting dynamics and the underlying biophysical and biochemical processes critically depends on computational models of intra-cellular transport. A number of motor-cargo models have hence been developed to reproduce experimentally observed transport dynamics on various levels of detail. Computer simulations of these models have so far exclusively relied on approximate time-discretization methods. Using a consensus motor-cargo model that unites several existing models from the literature we demonstrate that this simulation approach is not correct. The numerical errors do not vanish even for arbitrarily small time steps, rendering the algorithm inconsistent. We propose a novel exact simulation algorithm for intra-cellular transport models that is also computationally more efficient than the approximate one. Furthermore, we introduce a robust way of analyzing the different time scales in the model dynamics using velocity autocorrelation functions. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Hybrid stochastic-deterministic model Hybrid continuous-discrete simulation stochastic simulation algorithm Intra-cellular transport Motor proteins

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The study of physical education evaluation based on a fuzzy stochastic algorithm

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SOFT COMPUTING 2022年第16期26卷 7917-7923页

作者： Su, Xiuyan Zhengzhou Univ Econ & Business Dept Publ Teaching Zhengzhou 451191 Peoples R China

With the rapid development of Internet technology, data mining has been widely used in universities. At the same time, the rise of smart campuses makes the campus behaviors of college students begin to be stored in databases or data warehouses in the form of data. This article was based on stochastic simulation algorithm analysis and research the physical education teaching evaluation, and has proposed a teaching evaluation data of random simulation algorithm, and the evaluation data and the algorithm were applied to real teaching, teaching evaluation data in testing the noise data separation to separate the data in the table. In this paper, we have used the cosine similarity of the adjustment as the method to calculate the similarity between the cluster center in order to verify the effectiveness and efficiency of the algorithm, and the algorithm proposed in this paper is compared with the greedy algorithm and the frequency-based outlier detection algorithm. The proposed model is compared with the existing techniques to show the system's effectiveness for various performance measures. The proposed model is implemented in Matlab software, and the results are validated. The experiment results have shown better results for the proposed algorithm with high efficiency and accuracy.

关键词： stochastic simulation algorithm Evaluation of physical education Noise data Random fuzzy algorithm Smart campus and simulation

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Multiscale stochastic simulations of chemical reactions with regulated scale separation

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JOURNAL OF COMPUTATIONAL PHYSICS 2013年 244卷 290-297页

作者： Koumoutsakos, Petros Feigelman, Justin ETH Chair Computat Sci CH-8092 Zurich Switzerland

We present a coupling of multiscale frameworks with accelerated stochastic simulation algorithms for systems of chemical reactions with disparate propensities. The algorithms regulate the propensities of the fast and slow reactions of the system, using alternating micro and macro sub-steps simulated with accelerated algorithms such as tau and R-leaping. The proposed algorithms are shown to provide significant speedups in simulations of stiff systems of chemical reactions with a trade-off in accuracy as controlled by a regulating parameter. More importantly, the error of the methods exhibits a cutoff phenomenon that allows for optimal parameter choices. Numerical experiments demonstrate that hybrid algorithms involving accelerated stochastic simulations can be, in certain cases, more accurate while faster, than their corresponding stochastic simulation algorithm counterparts. (C) 2012 Elsevier Inc. All rights reserved.

关键词： stochastic simulation algorithm Stiff systems Heterogeneous multiscale methods Flow averaging integrators Cutoff phenomenon

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stochastic delay differential equations for genetic regulatory networks

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2007年第2期205卷 696-707页

作者： Tian, Tianhai Burrage, Kevin Burrage, Pamela M. Carletti, Margherita Univ Queensland Adv Comp Modelling Ctr St Lucia Qld 4067 Australia Univ Urbino Inst Biomath Urbino Italy

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

关键词： stochastic delay differential equations genetic regulatory networks chemical Langevin equation stochastic simulation algorithm

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