Search results for "Laplace's method"

showing 10 items of 10 documents

Efficient estimation of generalized linear latent variable models.

2019

Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estim…

0106 biological sciencesMultivariate statisticsMultivariate analysisComputer scienceBinomials01 natural sciencesPolynomials010104 statistics & probabilityAmoebastilastolliset mallitestimointiProtozoansLikelihood FunctionsMultidisciplinaryApproximation MethodsStatistical ModelsSimulation and ModelingApplied MathematicsStatisticsQLinear modelREukaryotaLaplace's methodData Interpretation StatisticalPhysical SciencesVertebratesMedicineAlgorithmAlgorithmsResearch ArticleOptimizationScienceLatent variableResearch and Analysis Methods010603 evolutionary biologygeneralized linear latent variable modelsSet (abstract data type)BirdsAnimalsComputer Simulation0101 mathematicsta112OrganismsBiology and Life SciencesStatistical modelMarginal likelihoodAlgebraAmniotesMultivariate AnalysisLinear ModelsMathematicsSoftwarePLoS ONE
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Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

2021

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…

0106 biological sciencesStatistics and ProbabilityFOS: Computer and information sciences62F15 (Primary) 62M30 60G55 (Secondary)MCMCGaussianBayesian inferenceMarkovin ketjutStatistics - Applications010603 evolutionary biology01 natural sciencesCox processMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakeregeneraatio (biologia)Applied mathematicsApplications (stat.AP)0101 mathematicsLaplace approximationStatistics - MethodologyGeneral Environmental ScienceParametric statisticsMathematicsspatial random effectsbayesilainen menetelmäMarkov chain Monte CarloFunction (mathematics)15. Life on landMissing dataMonte Carlo -menetelmätcompetition kernelLaplace's methodKernel (statistics)symbolstree regenerationpuustometsänhoitomatemaattiset mallitStatistics Probability and Uncertainty
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Statistical methods for spatial cluster detection in childhood cancer incidence : A simulation study

2021

BACKGROUND AND OBJECTIVE: The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of such clusters may help to better understand etiology and develop preventive strategies. We evaluated widely used statistical approaches to cluster detection in this context.; METHODS: Incidence of newly diagnosed childhood cancer (140/1,000,000 children under 15 years) and nephroblastoma (7/1,000,000) was simulated. Clusters of defined size (1-50) were randomly assembled on the district level in Germany. Each cluster was simulated with different relative risk levels (1-100). For each combination 2000 iterations were done. Simulated data was then analyzed …

Cancer ResearchEpidemiologyScan statisticBayesian probabilityMedizinContext (language use)03 medical and health sciences0302 clinical medicineNeoplasmsStatisticsMedicineCluster AnalysisHumans030212 general & internal medicineSensitivity (control systems)Cluster analysisChildbusiness.industryIncidence (epidemiology)IncidenceIdentification (information)OncologyLaplace's method030220 oncology & carcinogenesisFemalebusiness
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Path Integral approach via Laplace’s method of integration for nonstationary response of nonlinear systems

2019

In this paper the nonstationary response of a class of nonlinear systems subject to broad-band stochastic excitations is examined. A version of the Path Integral (PI) approach is developed for determining the evolution of the response probability density function (PDF). Specifically, the PI approach, utilized for evaluating the response PDF in short time steps based on the Chapman–Kolmogorov equation, is here employed in conjunction with the Laplace’s method of integration. In this manner, an approximate analytical solution of the integral involved in this equation is obtained, thus circumventing the repetitive integrations generally required in the conventional numerical implementation of …

Computer sciencePath IntegralMonte Carlo methodMarkov processProbability density function02 engineering and technologyNonstationary response01 natural sciencessymbols.namesake0203 mechanical engineering0103 physical sciencesProbability density functionApplied mathematics010301 acousticsVan der Pol oscillatorLaplace transformMechanical EngineeringEvolutionary excitationLaplace’s methodCondensed Matter PhysicsNonlinear system020303 mechanical engineering & transportsMechanics of MaterialsLaplace's methodPath integral formulationsymbolsSettore ICAR/08 - Scienza Delle Costruzioni
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Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.

2015

Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covar…

Health (social science)Computer scienceEpidemiologyGaussian030231 tropical medicineGeography Planning and DevelopmentBayesian probabilityNormal Distributionlcsh:G1-922Medicine (miscellaneous)Bayesian inference01 natural sciencesNormal distribution010104 statistics & probability03 medical and health sciencessymbols.namesakeBayes' theorem0302 clinical medicineCovariateStatisticsINLAHierarchical Bayesian modellingEconometricsHumansGeostatistics0101 mathematicsSpatial AnalysisStochastic ProcessesModels StatisticalHealth PolicyBayes TheoremFasciola hepaticaLaplace's methodsymbolsGaussian network modelBayesian Kriginglcsh:Geography (General)Geospatial health
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Statistical Properties of Double Hoyt Fading With Applications to the Performance Analysis of Wireless Communication Systems

2018

In this paper, we investigate the statistical properties of double Hoyt fading channels, where the overall received signal is determined by the product of two statistically independent but not necessarily identically distributed single Hoyt processes. Finite-range integral expressions are first derived for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades of the envelope fading process. A closed-form approximate solution is also deduced for the LCR by making use of the Laplace approximation theorem. Applying the derived PDF of the double Hoyt channel, we then provide analytical expressions for the average…

Independent and identically distributed random variablesGeneral Computer ScienceGaussianProbability density function02 engineering and technologyDouble Hoyt fading channel modelsymbols.namesake0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceFadingGaussian processMathematicsComputer Science::Information TheoryCumulative distribution function020208 electrical & electronic engineeringMathematical analysisGeneral Engineering020206 networking & telecommunicationsvehicular-to-vehicular (V2V) channelsLaplace's methodprobability density function (PDF)symbolsaverage duration of fades (ADF)cumulative distribution function (CDF)lcsh:Electrical engineering. Electronics. Nuclear engineeringlevel-crossing rate (LCR)lcsh:TK1-9971Quadrature amplitude modulationIEEE Access
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Laplace’s Method of Integration in the Path Integral Approach for the Probabilistic Response of Nonlinear Systems

2020

In this paper the response of nonlinear systems under stationary Gaussian white noise excitation is studied. The Path Integral (PI) approach, generally employed for evaluating the response Probability Density Function (PDF) of systems in short time steps based on the Chapman-Kolmogorov equation, is here used in conjunction with the Laplace’s method of integration. This yields an approximate analytical solution of the integral involved in the Chapman-Kolmogorov equation. Further, in this manner the repetitive integrations, generally required in the conventional numerical implementation of the procedure, can be circumvented. Application to a nonlinear system is considered, and pertinent compa…

Nonlinear systemPath Integral Laplace’s method Nonstationary response Probability density function.Laplace transformLaplace's methodPath integral formulationProbabilistic logicApplied mathematicsProbability density functionWhite noiseSettore ICAR/08 - Scienza Delle CostruzioniExcitationMathematics
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Methods of spatial cluster detection in rare childhood cancers: Benchmarking data and results from a simulation study on nephroblastoma

2021

Abstract The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of rare disease clusters in general may help to better understand disease etiology and develop preventive strategies against such entities. The incidence of newly diagnosed childhood malignancies under 15 years of age is 140/1,000,000. In this context, the subgroup of nephroblastoma represents an extremely rare entity with an annual incidence of 7/1,000,000. We evaluated widely used statistical approaches for spatial cluster detection in childhood cancer (Ref. [22] Schundeln et al., 2021, Cancer Epidemiology). For the simulation study, random high risk clusters of 1 to 50 ad…

Simulation studyComputer scienceScan statisticBayesian probabilityMedizinContext (language use)lcsh:Computer applications to medicine. Medical informaticsBayesian03 medical and health sciences0302 clinical medicineRandom distributionStatisticsCluster analysislcsh:Science (General)NephroblastomaData Article030304 developmental biology0303 health sciencesMultidisciplinaryBenchmarkingIdentification (information)Besag-NewellLaplace's methodSpatial clusterlcsh:R858-859.7Besag York MolliéRaw dataChildhood cancerSpatial scan statistic030217 neurology & neurosurgerylcsh:Q1-390Data in Brief
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Prior-based Bayesian information criterion

2019

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one ov…

Statistics and ProbabilityLaplace expansionApplied MathematicsBayes factorMarginal likelihoodStatistics::Computationsymbols.namesakeComputational Theory and MathematicsLaplace's methodBayesian information criterionPrior probabilitysymbolsApplied mathematicsStatistics::MethodologyStatistics Probability and UncertaintyLikelihood functionFisher informationAnalysisMathematics
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Self-stabilizing processes: uniqueness problem for stationary measures and convergence rate in the small-noise limit

2011

In the context of self-stabilizing processes, that is processes attracted by their own law, living in a potential landscape, we investigate different properties of the invariant measures. The interaction between the process and its law leads to nonlinear stochastic differential equations. In [S. Herrmann and J. Tugaut. Electron. J. Probab. 15 (2010) 2087–2116], the authors proved that, for linear interaction and under suitable conditions, there exists a unique symmetric limit measure associated to the set of invariant measures in the small-noise limit. The aim of this study is essentially to point out that this statement leads to the existence, as the noise intensity is small, of one unique…

Statistics and ProbabilityMcKean-Vlasov equationLaplace transformdouble-well potential010102 general mathematicsMathematical analysisFixed-point theoremfixed point theoremDouble-well potentialInvariant (physics)01 natural sciencesself-interacting diffusionuniqueness problem[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010104 statistics & probabilityRate of convergenceLaplace's methodUniquenessInvariant measureperturbed dynamical systemstationary measures0101 mathematicsLaplace's methodprimary 60G10; secondary: 60J60 60H10 41A60Mathematics
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