Search results for " Markov Chain"

showing 10 items of 52 documents

Simple epidemic model by a Markov chain

2008

In this paper we propose a continuous-time Markov chain to describe a SIs model with and without external reinfection

Settore MAT/05 - Analisi MatematicaEpidemic model Markov chain
researchProduct

Towards a fuzzy-linguistic based social network sentiment-expression system

2015

Liking allows users of Social Networks, blogs and online magazines to express their support of posts and artifacts by a simple click. Such function is very popular but lacks semantic power, and some platforms have augmented it by allowing to choose a pictographic depiction corresponding to a feeling. What is gained in depth is lost in simplicity, and the wide acceptance liking has enjoyed did not carried to the sentiment version. We outline a sentiment-expression hybrid system based on textual analysis and linguistic fuzzy Markov chains overcoming the intrinsic limitations of liking without burdening the user with complex choices.

Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceSentiment analysisSettore M-FIL/02 - Logica E Filosofia Della Scienzacomputer.software_genresocial networks sentiment analysis linguis- tic fuzzy Markov chainsExpression (architecture)Fuzzy linguisticArtificial intelligencebusinesscomputerNatural language processing
researchProduct

Multi-Phase epidemic model by a Markov chain

2008

Abstract In this paper we propose a continuous-time Markov chain to describe the spread of an infective and non-mortal disease into a community numerically limited and subjected to an external infection. We make a numerical simulation that shows tendencies for recurring epidemic outbreaks and for fade-out or extinction of the infection.

Statistics and ProbabilityExtinctionMarkov chainMulti phaseComputer scienceEpidemic models Markov chain Numerical simulationStatistical physicsCondensed Matter PhysicsEpidemic model
researchProduct

Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound?

2011

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (small) constant $\epsilon>0$ multiple of the identity matrix $I$. The lower bound on the eigenvalues of $S_n$ induced by the factor $\epsilon I$ is theoretically convenient, but practically cumbersome, as a good value for the parameter $\epsilon$ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of $S_n$ away …

Statistics and ProbabilityFOS: Computer and information sciencesIdentity matrixMathematics - Statistics TheoryStatistics Theory (math.ST)Upper and lower boundsStatistics - Computation93E3593E15Combinatorics60J27Mathematics::ProbabilityLaw of large numbers65C40 60J27 93E15 93E35stochastic approximationFOS: MathematicsEigenvalues and eigenvectorsComputation (stat.CO)Metropolis algorithmMathematicsProbability (math.PR)Zero (complex analysis)CovariancestabilityUniform continuityBounded function65C40Statistics Probability and Uncertaintyadaptive Markov chain Monte CarloMathematics - Probability
researchProduct

On the stability and ergodicity of adaptive scaling Metropolis algorithms

2011

The stability and ergodicity properties of two adaptive random walk Metropolis algorithms are considered. The both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted scaling parameter is not constrained within a predefined compact interval. The first algorithm is based on scale adaptation only, while the second one incorporates also covariance adaptation. A strong law of large numbers is shown to hold assuming that the target density is smooth enough and has either compact support or super-exponentially decaying tails.

Statistics and ProbabilityStochastic approximationMathematics - Statistics TheoryStatistics Theory (math.ST)Law of large numbersMultiple-try Metropolis01 natural sciencesStability (probability)010104 statistics & probabilityModelling and Simulation65C40 60J27 93E15 93E35Adaptive Markov chain Monte CarloFOS: Mathematics0101 mathematicsScalingMetropolis algorithmMathematicsta112Applied Mathematics010102 general mathematicsRejection samplingErgodicityProbability (math.PR)ta111CovarianceRandom walkMetropolis–Hastings algorithmModeling and SimulationAlgorithmStabilityMathematics - ProbabilityStochastic Processes and their Applications
researchProduct

MODERATE DEVIATION PRINCIPLES FOR BIFURCATING MARKOV CHAINS: CASE OF FUNCTIONS DEPENDENT OF ONE VARIABLE

2021

The main purpose of this article is to establish moderate deviation principles for additive functionals of bifurcating Markov chains. Bifurcating Markov chains are a class of processes which are indexed by a regular binary tree. They can be seen as the models which represent the evolution of a trait along a population where each individual has two offsprings. Unlike the previous results of Bitseki, Djellout \& Guillin (2014), we consider here the case of functions which depend only on one variable. So, mainly inspired by the recent works of Bitseki \& Delmas (2020) about the central limit theorem for general additive functionals of bifurcating Markov chains, we give here a moderate deviatio…

Statistics and Probability[MATH.MATH-PR]Mathematics [math]/Probability [math.PR][MATH.MATH-PR] Mathematics [math]/Probability [math.PR]60J80Bifurcating Markov chainsbinary trees[MATH]Mathematics [math]binary trees Mathematics Subject Classification (2020): 60F10deviation inequalitiesMathematics - Probabilitymoderate deviation principles
researchProduct

Contributed discussion on article by Pratola

2016

The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.

Statistics and Probabilitymodel selectionMarkov Chain Monte Carlo (MCMC)Bayesian regression treeComputer scienceBig dataBayesian regression tree (BRT) modelsComputingMilieux_LEGALASPECTSOFCOMPUTINGbirth–death processMachine learningcomputer.software_genreSequential Monte Carlo methods01 natural sciencespopulation Markov chain Monte Carlo010104 statistics & probabilitysymbols.namesakebig data0502 economics and businessBayesian Regression Trees (BART)0101 mathematics050205 econometrics Bayesian treed regressionMultiple Try Metropolis algorithmsINFERÊNCIA ESTATÍSTICAbusiness.industryApplied MathematicsModel selection05 social sciencesRejection samplingData scienceVariable-order Bayesian networkTree (data structure)Tree traversalMarkov chain Monte Carlocontinuous time Markov processsymbolsArtificial intelligencebusinessBayesian linear regressioncommunication-freecomputerGibbs samplingBayesian Analysis
researchProduct

European Option Pricing and Hedging with Both Fixed and Proportional Transaction Costs

2003

Abstract In this paper we provide a systematic treatment of the utility based option pricing and hedging approach in markets with both fixed and proportional transaction costs: we extend the framework developed by Davis et al. (SIAM J. Control Optim., 31 (1993) 470) and formulate the option pricing and hedging problem. We propose and implement a numerical procedure for computing option prices and corresponding optimal hedging strategies. We present a careful analysis of the optimal hedging strategy and elaborate on important differences between the exact hedging strategy and the asymptotic hedging strategy of Whalley and Wilmott (RISK 7 (1994) 82). We provide a simulation analysis in order …

Stochastic controlTransaction costEconomics and EconometricsMathematical optimizationControl and OptimizationApplied MathematicsMonte Carlo methods for option pricingjel:C61Implied volatilityjel:G13jel:G11option pricing transaction costs stochastic control Markov chain approximationMicroeconomicsVariable pricingOrder (business)Valuation of optionsEconomicsAsian optionFinite difference methods for option pricingSSRN Electronic Journal
researchProduct

A methodology and algorithms for an optimal identification of Tourist Local Systems

2007

In last years, despite the emphasis on the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the identification, the promotion and the governance of Tourism Local Systems (TLS). Moreover, nowadays an important debate is more and more emerging on the sustainable tourism development which involve three interconnected aspects: environmental, socio-cultural and economic. To this end, in this paper, a rigorous mathematical model is proposed for the optimal identification and dimensioning of TLS. The model here presented consists of a two stage methodology: at first, all the factors that characterize a geograph…

Tourist Local Systems Markov Chain Decision Trees Dynamic Programming
researchProduct

Statistics of transitions for Markov chains with periodic forcing

2013

The influence of a time-periodic forcing on stochastic processes can essentially be emphasized in the large time behaviour of their paths. The statistics of transition in a simple Markov chain model permits to quantify this influence. In particular the first Floquet multiplier of the associated generating function can be explicitly computed and related to the equilibrium probability measure of an associated process in higher dimension. An application to the stochastic resonance is presented.

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Markov chain mixing timeMarkov kernelMarkov chainProbability (math.PR)Markov chainlarge time asymptoticStochastic matrixcentral limit theoremMarkov process[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]symbols.namesakeMarkov renewal processModeling and SimulationFloquet multipliersStatisticsFOS: MathematicssymbolsMarkov propertyExamples of Markov chainsstochastic resonance60J27 60F05 34C25[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilityMathematics
researchProduct