Search results for "Monte Carlo methods"

showing 6 items of 26 documents

Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

2020

The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each…

Statistics and ProbabilityComputer sciencebusiness.industryBayesian probabilitySequential monte carlo methodsMachine learningcomputer.software_genre01 natural sciencesField (computer science)010104 statistics & probability03 medical and health sciences0302 clinical medicineEvent data030220 oncology & carcinogenesisStatistical analysisPersonalized medicineArtificial intelligence0101 mathematicsStatistics Probability and UncertaintybusinessJoint (audio engineering)CartographycomputerStatistical Modelling
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Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes

1999

A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.

Statistics and ProbabilityMathematical optimizationposterior mode estimatorMarkov chain Monte Carlo methodsMonte Carlo methodBayesian probabilityRejection samplingEstimatorMarkov chain Monte CarloBayesian smoothingGibbs processesHybrid Monte Carlosymbols.namesakeMarquardt algorithmsymbolspair potential functionPair potentialAlgorithmMathematicsGibbs samplingBernoulli
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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
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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
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Dosimetric evaluation of internal shielding in a high dose rate skin applicator

2011

Purpose: The Valencia HDR applicators are accessories of the microSelectron HDR afterloading system (Nucletron) shaped as truncated cones. The base of the cone is either 2 or 3 cm diameter. They are intended to treat skin lesions, being the typical prescription depth 3 mm. In patients with eyelid lesions, an internal shielding is very useful to reduce the dose to the ocular globe. The purpose of this work was to evaluate the dose enhancement from potential backscatter and electron contamination due to the shielding. Material and methods: Two methods were used: a) Monte Carlo simulation, performed with the GEANT4 code, 2 cm Valencia applicator was placed on the surface of a water phantom in …

lcsh:Rlcsh:MedicineOriginal Articleskin brachytherapyMonte Carlo methodsfilm dosimetryJournal of Contemporary Brachytherapy
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Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models

2017

Abstract European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like the Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference discretization leads to a so-called full order model (FOM). Reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD). The early exercise constraint of American options is enforced by a penalty on subset of grid points. The presented numerical experiments demonstrate that pricing with ROMs can be orders of magnitude faster within a give…

ta113Mathematical optimizationGeneral Computer ScienceStochastic volatilityDifferential equationEuropean optionMonte Carlo methods for option pricingJump diffusion010103 numerical & computational mathematics01 natural sciencesTheoretical Computer Science010101 applied mathematicsValuation of optionsModeling and Simulationlinear complementary problemRange (statistics)Asian optionreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingMathematicsJournal of Computational Science
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