Relevant bibliographies by topics / Discrete multivariate model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discrete multivariate model'

Author: Grafiati
Published: 30 November 2024

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Journal articles on the topic "Discrete multivariate model"

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Barr, Aiala. "A multivariate model for discrete data sets." Communications in Statistics - Theory and Methods 18, no. 2 (January 1989): 445–59. http://dx.doi.org/10.1080/03610928908829910.

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Barbiero, Alessandro. "Estimating a multivariate model with discrete Weibull margins." Journal of Statistical Theory and Practice 11, no. 4 (February 8, 2017): 503–14. http://dx.doi.org/10.1080/15598608.2017.1292483.

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Zilko, Aurelius A., and Dorota Kurowicka. "Copula in a multivariate mixed discrete–continuous model." Computational Statistics & Data Analysis 103 (November 2016): 28–55. http://dx.doi.org/10.1016/j.csda.2016.02.017.

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Zhang, Ke. "Multivariate discrete grey model base on dummy drivers." Grey Systems: Theory and Application 6, no. 2 (August 1, 2016): 246–58. http://dx.doi.org/10.1108/gs-09-2015-0051.

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Purpose – The purpose of this paper is to solve the problem that the qualitative relative factors cannot be employed in traditional multivariate grey models. Design/methodology/approach – First, a new model is constructed though introducing dummy drivers. Then, the parameters estimation method and recursive function of the model are discussed. Furthermore, dummy driver setting, pre and post test methods of dummy drivers are proposed. At last, the per capita income forecasting of rural residents in Henan province of China is solved with the proposed model. Findings – The proposed model is the reasonable extension of original one. The accuracy of it is higher than former model. In the case study, the forecasting results of proposed model are compared with other grey forecasting models, and prove that proposed model has not only high accuracy, but also clear physical meaning. Practical implications – The method proposed in the paper could be used in policy effect measure, marketing forecasting, etc., when the predictor variables are influenced by some qualitative variables. Originality/value – It will promote the accuracy of multivariate grey forecasting model.
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Douglas, Jeffrey A., Michael R. Kosorok, and Betty A. Chewning. "A latent variable model for discrete multivariate psychometric waiting times." Psychometrika 64, no. 1 (March 1999): 69–82. http://dx.doi.org/10.1007/bf02294320.

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Liu, Jiwei. "Test of Ordered Multivariate Discrete Selection Model for Average Life Expectancy." Journal of Applied Mathematics and Physics 10, no. 02 (2022): 261–69. http://dx.doi.org/10.4236/jamp.2022.102020.

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Golob, Thomas F., and Amelia C. Regan. "Trucking industry adoption of information technology: a multivariate discrete choice model." Transportation Research Part C: Emerging Technologies 10, no. 3 (June 2002): 205–28. http://dx.doi.org/10.1016/s0968-090x(02)00006-2.

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Ma, Xin, Mei Xie, Wenqing Wu, Bo Zeng, Yong Wang, and Xinxing Wu. "The novel fractional discrete multivariate grey system model and its applications." Applied Mathematical Modelling 70 (June 2019): 402–24. http://dx.doi.org/10.1016/j.apm.2019.01.039.

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Lim, Yaeji, Ying Kuen Cheung, and Hee-Seok Oh. "A generalization of functional clustering for discrete multivariate longitudinal data." Statistical Methods in Medical Research 29, no. 11 (May 5, 2020): 3205–17. http://dx.doi.org/10.1177/0962280220921912.

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This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.
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Bai, Hao, Yuan Zhong, Xin Gao, and Wei Xu. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method." Stats 3, no. 3 (July 15, 2020): 203–20. http://dx.doi.org/10.3390/stats3030016.

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In clinical research, study outcomes usually consist of various patients’ information corresponding to the treatment. To have a better understanding of the effects of different treatments, one often needs to analyze multiple clinical outcomes simultaneously, while the data are usually mixed with both continuous and discrete variables. We propose the multivariate mixed response model to implement statistical inference based on the conditional grouped continuous model through a pairwise composite-likelihood approach. It can simplify the multivariate model by dealing with three types of bivariate models and incorporating the asymptotical properties of the composite likelihood via the Godambe information. We demonstrate the validity and the statistic power of the multivariate mixed response model through simulation studies and clinical applications. This composite-likelihood method is advantageous for statistical inference on correlated multivariate mixed outcomes.
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Dissertations / Theses on the topic "Discrete multivariate model"

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Dong, Fanglong. "Bayesian Model Checking in Multivariate Discrete Regression Problems." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230.

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Wu, Hao. "Probabilistic Modeling of Multi-relational and Multivariate Discrete Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74959.

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Modeling and discovering knowledge from multi-relational and multivariate discrete data is a crucial task that arises in many research and application domains, e.g. text mining, intelligence analysis, epidemiology, social science, etc. In this dissertation, we study and address three problems involving the modeling of multi-relational discrete data and multivariate multi-response count data, viz. (1) discovering surprising patterns from multi-relational data, (2) constructing a generative model for multivariate categorical data, and (3) simultaneously modeling multivariate multi-response count data and estimating covariance structures between multiple responses. To discover surprising multi-relational patterns, we first study the ``where do I start?'' problem originating from intelligence analysis. By studying nine methods with origins in association analysis, graph metrics, and probabilistic modeling, we identify several classes of algorithmic strategies that can supply starting points to analysts, and thus help to discover interesting multi-relational patterns from datasets. To actually mine for interesting multi-relational patterns, we represent the multi-relational patterns as dense and well-connected chains of biclusters over multiple relations, and model the discrete data by the maximum entropy principle, such that in a statistically well-founded way we can gauge the surprisingness of a discovered bicluster chain with respect to what we already know. We design an algorithm for approximating the most informative multi-relational patterns, and provide strategies to incrementally organize discovered patterns into the background model. We illustrate how our method is adept at discovering the hidden plot in multiple synthetic and real-world intelligence analysis datasets. Our approach naturally generalizes traditional attribute-based maximum entropy models for single relations, and further supports iterative, human-in-the-loop, knowledge discovery. To build a generative model for multivariate categorical data, we apply the maximum entropy principle to propose a categorical maximum entropy model such that in a statistically well-founded way we can optimally use given prior information about the data, and are unbiased otherwise. Generally, inferring the maximum entropy model could be infeasible in practice. Here, we leverage the structure of the categorical data space to design an efficient model inference algorithm to estimate the categorical maximum entropy model, and we demonstrate how the proposed model is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and US census datasets, and demonstrate its feasibility using an epidemic simulation application. Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses. Existing methods for univariate count responses cannot be easily extended to the multivariate case since the dependency among multiple responses needs to be properly accounted for. To model multivariate data with multiple count responses, we propose a novel multivariate Poisson log-normal model (MVPLN). By simultaneously estimating the regression coefficients and inverse covariance matrix over the latent variables with an efficient Monte Carlo EM algorithm, the proposed model takes advantages of association among multiple count responses to improve the model prediction accuracy. Simulation studies and applications to real world data are conducted to systematically evaluate the performance of the proposed method in comparison with conventional methods.
Ph. D.
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Zheng, Xiyu. "SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4403.

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Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way to analyze all available data in the model is to estimate a multivariate normal joint distribution of variables, including the outcome, subject to missingness conditional on covariates completely observed by maximum likelihood(ML); draw multiple imputation (MI) of missing values given the estimated joint model; and analyze the hierarchical model given the MI [1,2]. The assumption is data missing at random (MAR). While this method yields efficient estimation of the hierarchical model, it often estimates the model given discrete missing data that is handled under multivariate normality. In this thesis, we evaluate how robust it is to estimate a hierarchical linear model given discrete missing data by the method. We simulate incompletely observed data from a series of hierarchical linear models given discrete covariates MAR, estimate the models by the method, and assess the sensitivity of handling discrete missing data under the multivariate normal joint distribution by computing bias, root mean squared error, standard error, and coverage probability in the estimated hierarchical linear models via a series of simulation studies. We want to achieve the following aim: Evaluate the performance of the method handling binary covariates MAR. We let the missing patterns of level-1 and -2 binary covariates depend on completely observed variables and assess how the method handles binary missing data given different values of success probabilities and missing rates. Based on the simulation results, the missing data analysis is robust under certain parameter settings. Efficient analysis performs very well for estimation of level-1 fixed and random effects across varying success probabilities and missing rates. MAR estimation of level-2 binary covariate is not well estimated when the missing rate in level-2 binary covariate is greater than 10%. The rest of the thesis is organized as follows: Section 1 introduces the background information including conventional methods for hierarchical missing data analysis, different missing data mechanisms, and the innovation and significance of this study. Section 2 explains the efficient missing data method. Section 3 represents the sensitivity analysis of the missing data method and explain how we carry out the simulation study using SAS, software package HLM7, and R. Section 4 illustrates the results and useful recommendations for researchers who want to use the missing data method for binary covariates MAR in HLM2. Section 5 presents an illustrative analysis National Growth of Health Study (NGHS) by the missing data method. The thesis ends with a list of useful references that will guide the future study and simulation codes we used.
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Yildirak, Sahap Kasirga. "The Identificaton Of A Bivariate Markov Chain Market Model." Phd thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/1257898/index.pdf.

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This work is an extension of the classical Cox-Ross-Rubinstein discrete time market model in which only one risky asset is considered. We introduce another risky asset into the model. Moreover, the random structure of the asset price sequence is generated by bivariate finite state Markov chain. Then, the interest rate varies over time as it is the function of generating sequences. We discuss how the model can be adapted to the real data. Finally, we illustrate sample implementations to give a better idea about the use of the model.
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Valiquette, Samuel. "Sur les données de comptage dans le cadre des valeurs extrêmes et la modélisation multivariée." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS028.

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Cette thèse s’intéresse à certains aspects théoriques de la modélisation des données de comptage. Deux cadres distincts sont abordés : celui des valeurs extrêmes et celui de la modélisation multivariée. Notre première contribution explore, en termes des comportements extrêmes, les liens existants entre le mélange Poisson et sa loi de mélange. Ce travail permet de caractériser et séparer plusieurs familles de lois de mélanges Poisson selon leur comportement en queue. Bien que ce travail soit théorique, nous discutons de son utilité d’un point de vue pratique, notamment pour le choix de la loi de mélange. Notre deuxième contribution porte sur une nouvelle classe de modèles multivariés dénommée Tree Pólya Splitting. Celle-ci repose sur une modélisation hiérarchique et suppose qu’une quantité aléatoire est répartie successivement selon une loi de Pólya à travers une structure d’arbre de partition. Dans ce travail, nous caractérisons les lois marginales univariées et multivariées, les moments factoriels, ainsi que les structures de dépendance (covariance/corrélation) qui en découlent. Nous mettons en évidence, à l’aide d’un jeu de données correspondant à l’abondance de trichoptères, l’intérêt de cette classe de modèles en comparant nos résultats à ceux obtenus, par exemple, avec des modèles de type Poisson log-normale multivariée. Nous concluons cette thèse en présentant diverses perspectives de recherche
This thesis focuses on certain theoretical aspects of counting data modeling. Two distinct frameworks are addressed: extreme values and multivariate modeling. Our first contribution explores, in terms of extreme behaviors, the existing connections between the Poisson mixture and its mixing distribution. This work allows us to characterize and discriminate several families of Poisson mixture according to their tail behavior. Although this work is theoretical, we discuss its practical utility, particularly regarding the choice of the mixing distribution. Our second contribution focuses on a new class of multivariate models called Tree Pólya Splitting. This class is based on hierarchical modeling and assumes that a random quantity is successively divided according to a Pólya distribution through a partition tree structure. In this work, we characterize univariate and multivariate marginal distributions, factorial moments, as well as the resulting dependency structures (covariance/correlation). Using a dataset corresponding to the abundance of Trichoptera, we highlight the interest of this class of models by comparing our results to those obtained, for example, with multivariate Poisson-lognormal models. We conclude this thesis by presenting various perspectives
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Comas, Cufí Marc. "Aportacions de l'anàlisi composicional a les mixtures de distribucions." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/664902.

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The present thesis is a compendium of three original works produced between 2014 and 2018. The papers have a common link: they are different contributions made by compositional data analysis to the study of the models based on mixtures of probability distributions. In brief, we could say that compositional data analysis is a methodology that consists of studying a sample of measures that are strictly positive from a relative point of view. Mixtures of distributions are a specific type of probability distribution defined to be the convex linear combination of other distributions
La present tesi representa un compendi de tres treballs originals realitzats durant els anys 2014-2018. Aquests treballs comparteixen un nexe comú: tots ells són diferents aportacions de l'anàlisi composicional a l'estudi dels models basats en mixtures de distribucions de probabilitat. D'una forma molt breu, podríem dir que l'anàlisi composicional és una metodologia consistent en estudiar una mostra de mesures estrictament positives des d'un punt de vista relatiu. Les mixtures de distribucions, també anomenades barreges de distribucions, són un tipus particular de distribucions de probabilitat definides com la combinació lineal convexa d'altres distribucions
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Wiberg, Viktor. "Terrain machine learning : A predictive method for estimating terrain model parameters using simulated sensors, vehicle and terrain." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149815.

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Predicting terrain trafficability of deformable terrain is a difficult task with applications in e.g, forestry, agriculture, exploratory missions. The currently used techniques are neither practical, efficient, nor sufficiently accurate and inadequate for certain soil types. An online method which predicts terrain trafficability is of interest for any vehicle with purpose to reduce ground damage, improve steering and increase mobility. This thesis presents a novel approach for predicting the model parameters used in modelling a virtual terrain. The model parameters include particle stiffness, tangential friction, rolling resistance and two parameters related to particle plasticity and adhesion. Using multi-body dynamics, both vehicle and terrain can be simulated, which allows for an efficient exploration of a great variety of terrains. A vehicle with access to certain sensors can frequently gather sensor data providing information regarding vehicle-terrain interaction. The proposed method develops a statistical model which uses the sensor data in predicting the terrain model parameters. However, these parameters are specified at model particle level and do not directly explain bulk properties measurable on a real terrain. Simulations were carried out of a single tracked bogie constrained to move in one direction when traversing flat, homogeneous terrains. The statistical model with best prediction accuracy was ridge regression using polynomial features and interaction terms of second degree. The model proved capable of predicting particle stiffness, tangential friction and particle plasticity, with moderate accuracy. However, it was deduced that the current predictors and training scenarios were insufficient in estimating particle adhesion and rolling resistance. Nevertheless, this thesis indicates that it should be possible to develop a method which successfully predicts terrain model properties.
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Siddiqi, Junaid Sagheer. "Mixture and latent class models for discrete multivariate data." Thesis, University of Exeter, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303877.

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Scott, Laurie Croslin Zeng Yong. "Bayesian inference via filtering of micro-movement multivariate stock price models with discrete noises." Diss., UMK access, 2006.

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Thesis (Ph. D.)--Dept. of Mathematics and Statistics and Dept. of Economics. University of Missouri--Kansas City, 2006.
"A dissertation in mathematics and economics." Advisor: Yong Zeng. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed Jan. 29, 2007. Includes bibliographical references (leaves 121-124). Online version of the print edition.
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Olson, Brent. "Evaluating the error of measurement due to categorical scaling with a measurement invariance approach to confirmatory factor analysis." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/332.

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It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed.
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Books on the topic "Discrete multivariate model"

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E, Fienberg Stephen, and Holland Paul W, eds. Discrete multivariate analysis: Theory and practice. Cambridge, Mass: MIT Press, 1988.

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Mullahy, John. Marginal effects in multivariate probit and kindred discrete and count outcome models, with applications in health economics. Cambridge, MA: National Bureau of Economic Research, 2011.

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Zelterman, Daniel. Models for Discrete Data. Oxford University Press, 2006.

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Zelterman, Daniel. Models for Discrete Data. Ebsco Publishing, 2006.

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Holland, Paul W., Yvonne M. Bishop, and Stephen Fienberg. Discrete Multivariate Analysis. Springer, 2008.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Gelfand, Alan, and Sujit K. Sahu. Models for demography of plant populations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.17.

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This article discusses the use of Bayesian analysis and methods to analyse the demography of plant populations, and more specifically to estimate the demographic rates of trees and how they respond to environmental variation. It examines data from individual (tree) measurements over an eighteen-year period, including diameter, crown area, maturation status, and survival, and from seed traps, which provide indirect information on fecundity. The multiple data sets are synthesized with a process model where each individual is represented by a multivariate state-space submodel for both continuous (fecundity potential, growth rate, mortality risk, maturation probability) and discrete states (maturation status). The results from plant population demography analysis demonstrate the utility of hierarchical modelling as a mechanism for the synthesis of complex information and interactions.
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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Book chapters on the topic "Discrete multivariate model"

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Bayesian Causal Network for Discrete Variables." In Intelligent Control and Learning Systems, 233–49. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_13.

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AbstractEnsuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.
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Wackernagel, Hans. "Kriging with Discrete Point-Bloc Models." In Multivariate Geostatistics, 273–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5_36.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Investigative Purposes." In Bayes Factors for Forensic Decision Analyses with R, 141–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_4.

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AbstractThis chapter develops and discusses Bayes factors for investigative purposes, i.e. situations in which no potential source is available for comparison purposes. A typical example for this is the problem of classifying items or individuals into one of several classes or populations on the basis of available data (e.g., measurements of one or more attributes). More specifically, material of interest is analyzed (e.g., the quantity of cocaine present on banknotes) and results are evaluated in terms of their effect on the odds in favor of a proposition according to which the recovered material originates from a given population (e.g., banknotes in general circulation), compared to an alternative proposition according to which the recovered items originate from another population (e.g., banknotes related to drug trafficking). The problem of discrimination between populations is addressed for various types of discrete and continuous data, respectively, including an extension to continuous multivariate data. The examples developed in this chapter involve classification for two or more populations. The assessment of model performance is addressed as well.
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Zelterman, Daniel, and Chang Yu. "Multivariate Discrete Models for Longevity in Twins." In Diagnosis and Prediction, 131–40. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1540-0_8.

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Nikoloulopoulos, Aristidis K. "Copula-Based Models for Multivariate Discrete Response Data." In Copulae in Mathematical and Quantitative Finance, 231–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35407-6_11.

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Ferrer, Emilio. "Discrete- and Semi-continuous Time Latent Change Score Models of Fluid Reasoning Development from Childhood to Adolescence 1." In Longitudinal Multivariate Psychology, 38–60. New York, NY : Routledge, 2019. | Series: Multivariate applications series |: Routledge, 2018. http://dx.doi.org/10.4324/9781315160542-3.

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Rosenwasser, Efim N., Bernhard P. Lampe, and Torsten Jeinsch. "Parametric Discrete Models of Multivariable Continuous Processes with Delay." In Computer-Controlled Systems with Delay, 195–233. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15042-6_5.

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Kőrösi, Gábor, and Richárd Farkas. "Deep Learning Models and Interpretations for Multivariate Discrete-Valued Event Sequence Prediction." In Lecture Notes in Computer Science, 396–406. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86380-7_32.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Evaluative Purposes." In Bayes Factors for Forensic Decision Analyses with R, 79–139. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_3.

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AbstractThis chapter presents and discusses the use of the Bayes factor for the evaluation of scientific evidence in the form of discrete, continuous and continuous multivariate data. The latter may present a complex dependence structure that will be handled by means of multilevel models. The notion of “evaluative purpose” is understood here as referring to situations in which material of known source (control material) and evidential material of unknown source (recovered or questioned material) is collected and analyzed. The purpose is to evaluate the effect of the output of the examinations, in the form of scores or measurements of features, on the odds in favor of a proposition put forward by the prosecution, compared to an alternative proposition advanced by the defence. A discussion is included of the sensitivity of the described Bayes factor procedures to changes in the features of recovered and control materials, the available background information, as well as to choices made during probabilistic modelling and prior elicitation.
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Edwards, D. "Graphical Modelling." In Recent Advances In Descriptive Multivariate Analysis, 135–56. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198522850.003.0007.

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Abstract The aim of this chapter is to provide a non-technical overview of graphical modelling. Independence graphs with both lines (undirected edges) and arrows (directed edges) are described, together with associated models in duding both discrete and continuous variables. Some discussion of causal inference is also given. Graphical modelling is a variant of statistical modelling that uses graphs to display models. From a historical perspective, the origins of the approach lie partly in path analysis, as introduced by Wright (1921), and partly in statistical physics, in work of Gibbs (1902). In contrast to most other types of statistical graphics, the graphs do not display data, but rather an interpretation of the data, in the form of a model. For example, Fig. 7.1 shows a model with three variables, longevity (length of life), sex and occupation. Edges connect sex and longevity, and sex and occupation, but not occupation and longevity.
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Conference papers on the topic "Discrete multivariate model"

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Ke Zhang and Qu Pinpin. "Multivariate Discrete Grey Model base on Dummy Drivers." In 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, 2015. http://dx.doi.org/10.1109/gsis.2015.7301866.

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Sorokina, Mariia, Stylianos Sygletos, and Sergei Turitsyn. "Perturbative discrete-time multivariate fiber channel model with finite memory." In 2016 18th International Conference on Transparent Optical Networks (ICTON). IEEE, 2016. http://dx.doi.org/10.1109/icton.2016.7550440.

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Hernandez, Jefferson, and Andres G. Abad. "Learning from multivariate discrete sequential data using a restricted Boltzmann machine model." In 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI). IEEE, 2018. http://dx.doi.org/10.1109/colcaci.2018.8484854.

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Su, JiMing, Yiping Yao, and Feng Zhu. "An adaptive approach for parallel discrete event simulation thread pool prediction." In 38th ECMS International Conference on Modelling and Simulation. ECMS, 2024. http://dx.doi.org/10.7148/2024-0352.

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The number of threads in the thread pool is a critical factor that significantly influences the efficiency of parallel execution in Parallel Discrete Event Simulation (PDES). However, current methodologies, including such as static configuration, iterative search, solution based on system state information, and machine learning primarily cater to the domain of parallel computing. These approaches fail to consider PDES-specific characteristics like logical clock synchronization and the interaction among various simulation parameters, thereby posing challenges in accurately predicting the optimal number of threads required for achieving peak performance in PDES. In response to this, this paper proposes an adaptive multivariate power coefficient probability prediction method that considers the interrelated parameters in PDES which collectively influence runtime behavior. The method models various factors affecting PDES efficiency as multivariate power coefficients, incorporating a probability model and bias term to capture the variability of the simulation system. It constructs a nonlinear correlation model between the number of threads and simulation speedup, and derives the number of threads by calculating the optimal speedup of PDES. Experimental results demonstrate that this method achieves an average relative error of 6.4% in predicting the optimal number of threads, with performance improvement achieved by utilizing these predicted threads reaching 93.28% compared to ideal performance improvement relative to serial execution.
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Elinger, Jared D., and Jonathan D. Rogers. "Information Theoretic Tools for Parameter Estimation and Model Order Reduction for Mechanical Systems." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70744.

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Parameter estimation and model order reduction (MOR) are important techniques used in the development of mechanical system models. A variety of classical parameter estimation and MOR methods are available for nonlinear systems but performance generally suffers when little is known about the system model a priori. Recent advancements in information theory have yielded a quantity called causation entropy, which is a measure of the influence between multivariate time series. In parameter estimation problems involving dynamic systems, causation entropy can be used to identify which functions in a discrete-time model are important in driving the subsequent state values. This paper extends on previous works’ use of a Causation Entropy Matrix to nonlinear systems modeled from the real world. This work explores the conversion of continuous systems to a discrete model and applies the causation entropy matrix to the system. Results show that model structure can be estimated by the causation entropy matrix. This work extends the previous work by showing that the method can be applied to general nonlinear systems. Previously shown examples were toy, additively separable nonlinear problems. This work shows that the methodology can be extended to any nonlinear system, including time varying systems, which provides a framework to examine parameter estimation for general nonlinear systems.
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Shusheng, Zang, Zheng Hongtao, and Dong Keyong. "Simulation of the Dynamic Braking Process of Gas Turbine Propulsion System for a Ship." In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-170.

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Based on a ship driven by two controllable pitch propellers (CPP), one single CPP driven by one single gas turbine (GT), a mathematical model for a propulsion system is deduced, and its state-space model is established. In this paper, it is verified that the use of Phase-Linearized Discrete Simulation (PLDS) to estimate large perturbations is practicable. Especially for multivariate systems, this method appears to be rather convenient. We also present the results of simulation to the dynamic braking process of gas turbine propulsion system for a ship at the combined speed/power governing mode and the power governing mode of gas turbine.
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Joshi, Alok A., and Won-jong Kim. "System Identification and Multivariate Controller Design for a Satellite Ultraquiet Isolation Technology Experiment (SUITE)." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32024.

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A mathematical model of a six-degree-of-freedom hexapod system for vibration isolation was derived in the discrete-time domain on the basis of the experimental data obtained from a satellite. Using Box-Jenkins model structure, the transfer functions between six piezoelectric actuator input voltages and six geophone sensor output voltages are identified empirically. The 6×6 transfer function matrix is symmetric, and its off-diagonal terms indicate the coupling among different input/output channels. Though the coupling was observed among various input/output channels up to 10 Hz, the single-input single-output (SISO) controllers were designed neglecting the effect of coupling. The SISO controllers demonstrated limited performance in vibration attenuation. Using multi-input multi-output (MIMO) control techniques such as Linear Quadratic Gaussian (LQG) and H∞, high-order controllers were developed. The simulation results using these controllers obtain 33 dB, and 12 dB attenuation at 5, and 25 Hz corner frequencies, respectively.
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Hu, Zhen, and Sankaran Mahadevan. "Bayesian Network Learning for Uncertainty Quantification." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68187.

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Bayesian Networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new Multivariate Gaussian Mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their CPDs. The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.
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Liu, Defu, Yan Song, Hongda Shi, Yifa Yu, and Li Ma. "Poisson-Logistic Compound Bivariate Extreme Distribution and Its Application for Designing of Platform Deck Clearance." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37395.

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This paper proposes a new bivariate extreme probability model-Poisson-Logistic compound extreme distribution. Because the routes and frequencies of Typhoons vary each year, the frequencies of Typhoons occurring in certain sea areas also differ from year to year. This may cause a discrete distribution. Typhoon induced sea environments may be a kind of multivariate joint extreme distribution. By compounding the discrete distribution with a bivariate distribution of two extreme maritime factors, a new distribution-Poisson-Logistic compound bivariate extreme distribution is proposed in this paper. As one of the application example, a platform deck clearance can be estimated by compounding Typhoon induced maximum wave crest height and surge with 100 yrs joint occurring return period instead of traditional method, which proposed to use separate 100 yrs return period wave create height and surge.
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Xiao, Yuchen, Chuxi Liu, Wei Yu, Kamy Sepehrnoori, and Corwin Zigler. "Simultaneous Auto-Calibrations of Complex Fracture Configurations in Multi-Well Development Scenario in Uinta Basin with Embedded Discrete Fracture Model and Multivariate Gaussian Distributions." In Unconventional Resources Technology Conference. Tulsa, OK, USA: American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.15530/urtec-2022-3720388.

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Reports on the topic "Discrete multivariate model"

1

Mullahy, John. Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics. Cambridge, MA: National Bureau of Economic Research, November 2011. http://dx.doi.org/10.3386/w17588.

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