Relevant bibliographies by topics / Multivariate discrete probability

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

Academic literature on the topic 'Multivariate discrete probability'

Author: Grafiati
Published: 9 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "Multivariate discrete probability"

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Kafadar, Karen, Norman L. Johnson, Samuel Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Journal of the American Statistical Association 92, no. 440 (1997): 1654. http://dx.doi.org/10.2307/2965453.

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Wiegand, Martin, Saralees Nadarajah, and Yuanyuan Zhang. "Discrete analogues of continuous multivariate probability distributions." Annals of Operations Research 292, no. 1 (2020): 183–90. http://dx.doi.org/10.1007/s10479-020-03633-5.

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Poston, Wendy L., Norman L. Johnson, Samuel Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Technometrics 40, no. 2 (1998): 160. http://dx.doi.org/10.2307/1270659.

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Krummenauer, Frank. "Limit theorems for multivariate discrete distributions." Metrika 47, no. 1 (1998): 47–69. http://dx.doi.org/10.1007/bf02742864.

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Krummenauer, Frank. "Representation of multivariate discrete distributions by probability generating functions." Statistics & Probability Letters 39, no. 4 (1998): 327–31. http://dx.doi.org/10.1016/s0167-7152(98)00072-8.

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Vamvakari, Malvina. "On multivariate discrete q-Distributions-A multivariate q-Cauchy’s formula." Communications in Statistics - Theory and Methods 49, no. 24 (2019): 6080–95. http://dx.doi.org/10.1080/03610926.2019.1626427.

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Rodríguez, J., A. Conde, A. J. Sáez, and M. J. Olmo. "On discrete multivariate distributions symmetric in frequencies." Test 12, no. 2 (2003): 459–80. http://dx.doi.org/10.1007/bf02595725.

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Chou, Jine-Phone. "Simultaneous Estimation in Discrete Multivariate Exponential Families." Annals of Statistics 19, no. 1 (1991): 314–28. http://dx.doi.org/10.1214/aos/1176347984.

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Papageorgiou, H., N. L. Johnson, S. Kotz, and N. Balakrishnan. "Discrete Multivariate Distributions." Biometrics 54, no. 2 (1998): 795. http://dx.doi.org/10.2307/3109790.

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Zheng, Qi, and James H. Matis. "Approximating discrete multivariate distributions prom known moments." Communications in Statistics - Theory and Methods 22, no. 12 (1993): 3553–67. http://dx.doi.org/10.1080/03610929308831232.

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Dissertations / Theses on the topic "Multivariate discrete probability"

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Cannon, Paul C. "Extending the Information Partition Function: Modeling Interaction Effects in Highly Multivariate, Discrete Data." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1234.

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Because of the huge amounts of data made available by the technology boom in the late twentieth century, new methods are required to turn data into usable information. Much of this data is categorical in nature, which makes estimation difficult in highly multivariate settings. In this thesis we review various multivariate statistical methods, discuss various statistical methods of natural language processing (NLP), and discuss a general class of models described by Erosheva (2002) called generalized mixed membership models. We then propose extensions of the information partition function (IPF)
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Scutari, Marco. "Measures of Variability for Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3422736.

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In recent years, graphical models have been successfully applied in several different disciplines, including medicine, biology and epidemiology. This has been made possible by the rapid evolution of structure learning algorithms, from constraint-based ones to score-based and hybrid ones. The main goal in the development of these algorithms has been the reduction of the number of either independence tests or score comparisons needed to learn the structure of the Bayesian network. In most cases the characteristics of the learned networks have been studied using a small number of reference
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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,
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Kato, Fernando Hideki. "Análise de carteiras em tempo discreto." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-24022005-005812/.

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Nesta dissertação, o modelo de seleção de carteiras de Markowitz será estendido com uma análise em tempo discreto e hipóteses mais realísticas. Um produto tensorial finito de densidades Erlang será usado para aproximar a densidade de probabilidade multivariada dos retornos discretos uniperiódicos de ativos dependentes. A Erlang é um caso particular da distribuição Gama. Uma mistura finita pode gerar densidades multimodais não-simétricas e o produto tensorial generaliza este conceito para dimensões maiores. Assumindo que a densidade multivariada foi independente e identicamente distribuída (
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Montiel, Cendejas Luis Vicente. "Approximations, simulation, and accuracy of multivariate discrete probability distributions in decision analysis." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5031.

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Many important decisions must be made without full information. For example, a woman may need to make a treatment decision regarding breast cancer without full knowledge of important uncertainties, such as how well she might respond to treatment. In the financial domain, in the wake of the housing crisis, the government may need to monitor the credit market and decide whether to intervene. A key input in this case would be a model to describe the chance that one person (or company) will default given that others have defaulted. However, such a model requires addressing the lack of knowledge re
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Subasi, Munevver Mine. "New results in probability bounding, a convexity statement and unimodality of multivariate discrete distributions." 2008. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17395.

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Books on the topic "Multivariate discrete probability"

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

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

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Discrete Multivariate Analysis: Theory and Practice. Springer London, Limited, 2007.

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Balakrishnan, N., Norman L. Johnson, and Samuel Kotz. Four Volume Set: Univariate Discrete Distributions Second Edition Continuous Univariate Distributions Volume One Second Edition Continuous Univariate Distributions ... Edition Discrete Multivariate Distributions. 2nd ed. Wiley-Interscience, 1995.

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Voinov, V. G., and M. S. Nikulin. Unbiased Estimators and their Applications: Volume 2: Multivariate Case (Mathematics and Its Applications). Springer, 1996.

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Models for Discrete Longitudinal Data. Springer London, Limited, 2006.

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Models for Discrete Longitudinal Data. Springer New York, 2010.

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Models for Discrete Longitudinal Data (Springer Series in Statistics). Springer, 2005.

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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
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Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

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Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and the
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Book chapters on the topic "Multivariate discrete probability"

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Masry, Elias. "Multivariate Probability Density and Regression Functions Estimation of Continuous-Time Stationary Processes from Discrete-Time Data." In Stochastic Processes and Related Topics. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2030-5_17.

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Kovács, Edith, and Tamás Szántai. "On the Approximation of a Discrete Multivariate Probability Distribution Using the New Concept of t-Cherry Junction Tree." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03735-1_3.

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"Multivariate discrete distributions." In C&H/CRC Monographs on Statistics & Applied Probability. Chapman and Hall/CRC, 1997. http://dx.doi.org/10.1201/b13150-8.

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Good, I. J., and R. A. Gaskins. "Nonparametric roughness penalties for probability densities." In Biometrika. Oxford University PressOxford, 2001. http://dx.doi.org/10.1093/oso/9780198509936.003.0017.

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Abstract Given a number of observations x 1 … ,x N a nonparametric method is suggested for estimating the entire probability density curve. The method is to subtract a roughness penalty from the log likelihood, where the roughness penalty is a certain functional of the assumed density function √f. Those used are linear combinations of ∫γ′dx and ∫γ′′2dx where γ = f. The method appears to be consistent under wide conditions, although consistent methods can be rough. Numerical examples are given and show that for certain values of the coefficients in this linear expression the density function turns out to be very smooth even when N is small. Multivariate extensions are proposed, including one to distributions having some continuous and some discrete components, but numerical examples of these have not been tried. Some of the techniques are borrowed from quantum mechanics and tensor calculus.
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Hudson, Irene, Susan Won Sun Kim, and Marie Keatley. "Mixture Transition Distribution Modelling of Multivariate Time Series of Discrete State Processes: With an Application to Modelling Flowering Synchronisation with Respect to Climate Dynamics." In Probability, Combinatorics and Control. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88554.

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Conference papers on the topic "Multivariate discrete probability"

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Bello, Paolo, Agnese Pini, Simone Zazzini, Paolo Monti, and Giovanni Leuzzi. "Discrete multivariate probability distributions of microplastic settling/rising velocity in the marine environment." In 2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea). IEEE, 2022. http://dx.doi.org/10.1109/metrosea55331.2022.9950981.

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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 optima
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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.
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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
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