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Добірка наукової літератури з теми "INLA (Integrated Nested Laplace Approximation)"

Автор: Grafiati

Опубліковано: 13 липня 2022

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Статті в журналах з теми "INLA (Integrated Nested Laplace Approximation)":

Gómez-Rubio, Virgilio, Roger S. Bivand, and Håvard Rue. "Bayesian Model Averaging with the Integrated Nested Laplace Approximation." Econometrics 8, no. 2 (June 1, 2020): 23. http://dx.doi.org/10.3390/econometrics8020023.

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The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models.

Maulina, Retsi Firda, Anik Djuraidah, and Anang Kurnia. "PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA)." MEDIA STATISTIKA 12, no. 2 (December 30, 2019): 140. http://dx.doi.org/10.14710/medstat.12.2.140-151.

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Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.

Bilancia, Massimo, and Giacomo Demarinis. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)." Statistical Methods & Applications 23, no. 1 (October 2, 2013): 71–94. http://dx.doi.org/10.1007/s10260-013-0241-8.

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Ruiz-Cárdenas, Ramiro, Elias T. Krainski, and Håvard Rue. "Direct fitting of dynamic models using integrated nested Laplace approximations — INLA." Computational Statistics & Data Analysis 56, no. 6 (June 2012): 1808–28. http://dx.doi.org/10.1016/j.csda.2011.10.024.

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Illian, Janine B., Sigrunn H. Sørbye, and Håvard Rue. "A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)." Annals of Applied Statistics 6, no. 4 (December 2012): 1499–530. http://dx.doi.org/10.1214/11-aoas530.

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Morales-Otero, Mabel, and Vicente Núñez-Antón. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates." Mathematics 9, no. 3 (January 31, 2021): 282. http://dx.doi.org/10.3390/math9030282.

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In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

Abd Naeeim, Nurul Syafiah, and Nuzlinda Abdul Rahman. "Estimating relative risk for dengue disease in Peninsular Malaysia using INLA." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4 (December 26, 2017): 721–27. http://dx.doi.org/10.11113/mjfas.v0n0.575.

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Study in spatio-temporal disease mapping models give a great worth in epidemiology, in describing the pattern of disease incidence across geographical space and time. This paper studies generalized linear mixed models (GLMM) for the analysis of spatial and temporal variability of dengue disease rates. For spatio-temporal study, the models accommodate spatially correlated random effects as well as temporal effects together with the space time interaction. The space time interaction is used to capture any additional effects that are not explained by the main factors of space and time. However, as study including time dimension is quite complex for disease mapping, the temporal effects that only relate to structured and unstructured time pattern are considered in these models as initial screening in studying disease pattern and time trend. The models are fitted within a hierarchical Bayesian framework using Integrated Nested Laplace Approximation (INLA) methodology. For this study, there are three main objectives. First, to choose the best model that represent the disease phenomenon. Second, to estimate the relative risk of disease based on the model selected and lastly, to visualize the risk spatial pattern and temporal trend using graphical representation. The models are applied to monthly dengue fever data in Peninsular Malaysia reported to Ministry of Health Malaysia for year 2015 by district level.

Asmarian, Naeimehossadat, Seyyed Mohammad Taghi Ayatollahi, Zahra Sharafi, and Najaf Zare. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran." International Journal of Environmental Research and Public Health 16, no. 22 (November 13, 2019): 4460. http://dx.doi.org/10.3390/ijerph16224460.

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Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014.

SANTOS, Naiara Caroline Aparecido dos, and Jorge Luiz BAZÁN. "RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS." REVISTA BRASILEIRA DE BIOMETRIA 39, no. 1 (March 31, 2021): 206–20. http://dx.doi.org/10.28951/rbb.v39i1.531.

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A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items of tests that model the error (or success) count in several tasks over time, instead of modeling the correct responses to items in a test as in the dichotomous item response theory (IRT) model. These types of tests can be more informative than traditional tests. To estimate the model parameters, we consider a Bayesian approach using the integrated nested Laplace approximation (INLA). We develop residual analysis to assess model t by introducing randomized quantile residuals for items. The data used to illustrate the method comes from 228 people who took a selective attention test. The test has 20 blocks (items), with a time limit of 15 seconds for each block. The results of the residual analysis of the RPC were promising and indicated that the studied attention data are not well tted by the RPC model.

Maniatis, G., N. Demiris, A. Kranis, G. Banos, and A. Kominakis. "Comparison of inference methods of genetic parameters with an application to body weight in broilers." Archives Animal Breeding 58, no. 2 (July 27, 2015): 277–86. http://dx.doi.org/10.5194/aab-58-277-2015.

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Abstract. REML (restricted maximum likelihood) has become the standard method of variance component estimation in animal breeding. Inference in Bayesian animal models is typically based upon Markov chain Monte Carlo (MCMC) methods, which are generally flexible but time-consuming. Recently, a new Bayesian computational method, integrated nested Laplace approximation (INLA), has been introduced for making fast non-sampling-based Bayesian inference for hierarchical latent Gaussian models. This paper is concerned with the comparison of estimates provided by three representative programs (ASReml, WinBUGS and the R package AnimalINLA) of the corresponding methods (REML, MCMC and INLA), with a view to their applicability for the typical animal breeder. Gaussian and binary as well as simulated data were used to assess the relative efficiency of the methods. Analysis of 2319 records of body weight at 35 days of age from a broiler line suggested a purely additive animal model, in which the heritability estimates ranged from 0.31 to 0.34 for the Gaussian trait and from 0.19 to 0.36 for the binary trait, depending on the estimation method. Although in need of further development, AnimalINLA seems a fast program for Bayesian modeling, particularly suitable for the inference of Gaussian traits, while WinBUGS appeared to successfully accommodate a complicated structure between the random effects. However, ASReml remains the best practical choice for the serious animal breeder.

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Дисертації з теми "INLA (Integrated Nested Laplace Approximation)":

Ling, Yuheng. "Corsican housing market analysis : Applications of bayesian hierarchical model." Thesis, Corte, 2020. http://www.theses.fr/2020CORT0011.

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Ce travail de thèse porte sur le développement de modèles économétriques/statistiques spatiaux pour analyser le marché immobilier en Corse. Concernant les contributions techniques, j'aborde dans ce travail la question de l'autocorrélation spatiale et temporelle dans le résidu de la régression linéaire classique qui peut conduire à des estimations biaisées. Les premières études empiriques utilisant des outils « a-spatiaux », tels que la méthode des moindres carrés ordinaires, ont ainsi probablement produit des estimations biaisées. Grâce à l’adoption de techniques basées sur l'économétrie spatiale, les économistes peuvent désormais gérer de manière plus efficace les problèmes liés à la présence d'autocorrélations dans les données. Cependant, la prise en compte de la dimension temporelle dans ce type de modèles demeure « floue » en raison du recours à des paramètres complexes qu’elle nécessite. Pour faire face à l'autocorrelation spatiale et temporelle, j’ai eu recours à l'application de modèles spatiotemporels hiérarchiques bayésiens. En termes d'économie régionale, j’ai utilisé les modèles hiérarchiques spatiotemporels bayésiens que j’ai développés pour évaluer le marché immobilier en Corse. En particulier, la question de savoir en quoi l’emplacement géographique affecte les caractéristiques du logement (prix, destination principale) constitue le cœur de cette thèse. Les sujets analysés sont complexes car ils traitent de questions allant de la prévision des prix de vente des appartements en Corse, à l'enquête sur les taux des résidences secondaires et à l'évaluation de l'impact de la vue sur mer. En outre, les fondements économiques de ces thématiques reposent sur la méthode des prix hédoniques, la prise en compte d’effets adjacents (adjacent effects) et d’effets d’entrainement (ripple effects). Enfin, j'identifie les points chauds (hot spots) et les points froids (cold spots) en termes de prix des appartements et de taux des résidences secondaires, et j’évalue l’impact de la vue sur mer (la mer Méditerranée dans le cadre de ce travail) et de l'accessibilité à la côte sur les prix des appartements. Ces résultats devraient fournir de précieuses informations pouvant aider à la prise de décision des planificateurs en matière d’urbanisation et des décideurs publics
This thesis focuses on the development of spatial econometric/statistical models that are used for analyzing the Corsican real estate market.Concerning technical contributions, I address the issue of spatial and temporal autocorrelation in the residual of classical linear regression that may yield biased estimates. Early empirical studies using “spaceless” tools such as OLS probably yield biased estimates. With the acceptance of spatial econometrics, regional scientists can better handle the autocorrelation in data. However, the temporal dimension remains unclear due to its complex settings. To tackle both spatial and temporal autocorrelation, I suggest applying Bayesian hierarchical spatiotemporal models.Regarding the contribution in terms of regional economics, the developed ad-hoc Bayesian spatiotemporal hierarchical models have been used to assess the Corsican housing market. In particular, how locations affect housing is the key issue in this thesis. The topics analyzed are complex because they deal with issues ranging from predicting Corsican apartment sales prices, investigating second home rates to assessing the impact of sea views. Furthermore, the economic underpinnings of these topics include the hedonic price method, the adjacent effects and the ripple effects.Finally, I identify “hot spots” and “cold spots” in terms of apartment prices and second home rates, and I also indicate that both the sea (Mediterranean Sea) view and the coast accessibility affect apartment prices. These findings should provide valuable information for planners and policymakers

Schwarzenegger, Rafael. "Matematické modely spolehlivosti v technické praxi." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318802.

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Tato práce popisuje a aplikuje parametrické a neparametrické modely spolehlivosti na cenzorovaná data. Ukazuje implementaci spolehlivosti v metodologii Six Sigma. Metody jsou využity pro přežití/spolehlivost reálných technických dat.

Частини книг з теми "INLA (Integrated Nested Laplace Approximation)":

Riebler, Andrea, Mark D. Robinson, and Mark A. van de Wiel. "Analysis of Next Generation Sequencing Data Using Integrated Nested Laplace Approximation (INLA)." In Statistical Analysis of Next Generation Sequencing Data, 75–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07212-8_4.

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Gómez-Rubio, Virgilio. "The Integrated Nested Laplace Approximation." In Bayesian Inference with INLA, 13–38. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315175584-2.

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Тези доповідей конференцій з теми "INLA (Integrated Nested Laplace Approximation)":

Camponez, Marcelo Oliveira, Evandro O. Teatini Salles, and Mario Sarcinelli-Filho. "Applying integrated nested laplace approximation to the superresolution problem." In 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2011. http://dx.doi.org/10.1109/isspit.2011.6151568.

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Звіти організацій з теми "INLA (Integrated Nested Laplace Approximation)":

Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.

Girolamo Neto, Cesare, Rodolfo Jaffe, Rosane Cavalcante, and Samia Nunes. Comparacao de modelos para predicao do desmatamento na Amazonia brasileira. ITV, 2021. http://dx.doi.org/10.29223/prod.tec.itv.ds.2021.25.girolamoneto.

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O presente relatório contém resultados parciais do projeto “Definição de áreas prioritárias para recuperação florestal”, referentes a atividade “Uso e comparação da acurácia de diferentes modelos preditivos de desmatamento na Amazônia”. O objetivo deste estudo foi a implementação de modelos preditivos de desmatamento na Amazônia brasileira com base nas técnicas de Random Forest (RF), Spatial Random Forest (SpRF) e Integrated Nested Laplace Approximations (INLA) e comparação dos erros obtidos com cada modelo. Uma base de dados geográficos foi gerada por meio da integração de dados de diversas instituições brasileiras, como IBGE, MMA e INPE, utilizando células de 25 x 25 km e uma janela temporal de um ano. Os principais drivers de desmatamento identificados estão relacionados à fragmentação florestal e à expansão de áreas de pastagem na Amazônia, corroborando com outros trabalhos encontrados em literatura. A modelagem obteve melhores resultados com o uso dos modelos RF e SpRF em relação aos modelos do tipo INLA, com menores valores de erro médio quadrático obtido em conjuntos de dados de treinamento e validação dos algoritmos. A previsão de desmatamento para o ano de 2020 foi de 31 mil km2 , dados que apresentam uma superestimava devido ao método utilizado para o cálculo do desmatamento. Entre as ações identificadas que podem ser adotadas em trabalhos futuros para melhorar a previsão do desmatamento, cita-se o uso da abordagem CLUE e a melhoria de algumas bases de dados utilizada, a exemplo da malha viária.