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Journal articles on the topic 'Marginal probability'

To see the other types of publications on this topic, follow the link: Marginal probability.

Author: Grafiati

Published: 4 June 2021

Last updated: 24 January 2023

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1

Xiao, Yongshun. "THE MARGINAL PROBABILITY DENSITY FUNCTIONS OF WISHART PROBABILITY DENSITY FUNCTION." Far East Journal of Theoretical Statistics 54, no. 3 (May 1, 2018): 239–326. http://dx.doi.org/10.17654/ts054030239.

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2

Seneta, E. "Multivariate Probability in Terms of Marginal Probability and Correlation Coefficient." Biometrical Journal 29, no. 3 (1987): 375–80. http://dx.doi.org/10.1002/bimj.4710290321.

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3

Peng, Hanxiang, and Anton Schick. "Improving efficient marginal estimators in bivariate models with parametric marginals." Statistics & Probability Letters 79, no. 23 (December 2009): 2437–42. http://dx.doi.org/10.1016/j.spl.2009.08.022.

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4

AL-Moisheer, A. S., Refah Mohammed Alotaibi, Ghadah A. Alomani, and H. Rezk. "Bivariate Mixture of Inverse Weibull Distribution: Properties and Estimation." Mathematical Problems in Engineering 2020 (March 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/5234601.

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In this study, we construct a mixture of bivariate inverse Weibull distribution. We assumed that the parameters of two marginals have Bernoulli distributions. Several properties of the proposed model are obtained, such as probability marginal density function, probability marginal cumulative function, the product moment, the moment of the two variables x and y, the joint moment-generating function, and the correlation between x and y. The real dataset has been analyzed. We observed that the mixture bivariate inverse Weibull distribution provides a better fit than the other model.

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5

Leisink, M., and B. Kappen. "Bound Propagation." Journal of Artificial Intelligence Research 19 (August 1, 2003): 139–54. http://dx.doi.org/10.1613/jair.1130.

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In this article we present an algorithm to compute bounds on the marginals of a graphical model. For several small clusters of nodes upper and lower bounds on the marginal values are computed independently of the rest of the network. The range of allowed probability distributions over the surrounding nodes is restricted using earlier computed bounds. As we will show, this can be considered as a set of constraints in a linear programming problem of which the objective function is the marginal probability of the center nodes. In this way knowledge about the maginals of neighbouring clusters is passed to other clusters thereby tightening the bounds on their marginals. We show that sharp bounds can be obtained for undirected and directed graphs that are used for practical applications, but for which exact computations are infeasible.

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6

Picinbono, B. "ARMA Signals With Specified Symmetric Marginal Probability Distribution." IEEE Transactions on Signal Processing 58, no. 3 (March 2010): 1542–52. http://dx.doi.org/10.1109/tsp.2009.2037076.

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7

Cole, S. R., and M. A. Hernan. "Constructing Inverse Probability Weights for Marginal Structural Models." American Journal of Epidemiology 168, no. 6 (July 15, 2008): 656–64. http://dx.doi.org/10.1093/aje/kwn164.

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8

Nguyen, Truc T., and Allan R. Sampson. "The geometry of certain fixed marginal probability distributions." Linear Algebra and its Applications 70 (October 1985): 73–87. http://dx.doi.org/10.1016/0024-3795(85)90044-8.

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9

Gao, Wei, and Satoshi Kuriki. "Testing marginal homogeneity against stochastically ordered marginals for r×r contingency tables." Journal of Multivariate Analysis 97, no. 6 (July 2006): 1330–41. http://dx.doi.org/10.1016/j.jmva.2005.12.004.

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10

Hamada, Michiaki. "Direct Updating of an RNA Base-Pairing Probability Matrix with Marginal Probability Constraints." Journal of Computational Biology 19, no. 12 (December 2012): 1265–76. http://dx.doi.org/10.1089/cmb.2012.0215.

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11

CURTRIGHT, THOMAS, and COSMAS ZACHOS. "NEGATIVE PROBABILITY AND UNCERTAINTY RELATIONS." Modern Physics Letters A 16, no. 37 (December 7, 2001): 2381–85. http://dx.doi.org/10.1142/s021773230100576x.

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A concise derivation of all uncertainty relations is given entirely within the context of phase-space quantization, without recourse to operator methods, to the direct use of Weyl's correspondence, or to marginal distributions of x and p.

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12

Kalamkar, V. A. "Minification processes with discrete marginals." Journal of Applied Probability 32, no. 3 (September 1995): 692–706. http://dx.doi.org/10.2307/3215123.

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We investigate the stationarity of minification processes when the marginal is a discrete distribution. There is a close relationship between the problem considered by Arnold and Isaacson (1976) and the stationarity in minification processes. We give a necessary and sufficient condition for a discrete distribution to be the marginal of a stationary minification process. Members of the Poisson and negative binomial families can be the marginals of stationary minification processes. The geometric minification process is studied in detail, and two characterizations of it based on the structure of the innovation process are given.

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13

Masarotto, Guido, and Cristiano Varin. "Gaussian copula marginal regression." Electronic Journal of Statistics 6 (2012): 1517–49. http://dx.doi.org/10.1214/12-ejs721.

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14

Majerník, V. "Marginal probability distribution determined by the maximum entropy method." Reports on Mathematical Physics 45, no. 2 (April 2000): 171–81. http://dx.doi.org/10.1016/s0034-4877(00)89030-8.

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15

Austin, Peter C. "Inverse probability weighted estimation of the marginal odds ratio." Statistics in Medicine 27, no. 26 (November 20, 2008): 5560–63. http://dx.doi.org/10.1002/sim.3385.

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16

Weiss, Robert E., and Meehyung Cho. "Bayesian marginal influence assessment." Journal of Statistical Planning and Inference 71, no. 1-2 (August 1998): 163–77. http://dx.doi.org/10.1016/s0378-3758(98)00015-9.

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17

Saboor, Abdus, Hassan S. Bakouch, Fernando A. Moala, and Sheraz Hussain. "Properties and methods of estimation for a bivariate exponentiated Fréchet distribution." Mathematica Slovaca 70, no. 5 (October 27, 2020): 1211–30. http://dx.doi.org/10.1515/ms-2017-0426.

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AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimation of the parameters is investigated by the maximum likelihood with the observed information matrix. In addition to the maximum likelihood estimation method, we consider the Bayesian inference and least square estimation and compare these three methodologies for the BvEF. A simulation study is carried out to compare the performance of the estimators by the presented estimation methods. The proposed bivariate distribution with other related bivariate distributions are fitted to a real-life paired data set. It is shown that, the BvEF distribution has a superior performance among the compared distributions using several tests of goodness–of–fit.

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18

Hudson, Suzanne, and Paul W. Vos. "Marginal information for expectation parameters." Canadian Journal of Statistics 28, no. 4 (December 2000): 875–86. http://dx.doi.org/10.2307/3315922.

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19

Drton, Mathias, and Thomas S. Richardson. "Binary models for marginal independence." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, no. 2 (April 2008): 287–309. http://dx.doi.org/10.1111/j.1467-9868.2007.00636.x.

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20

Wellner, J. A. "Covariance formulas via marginal martingales." Statistica Neerlandica 48, no. 3 (November 1994): 201–7. http://dx.doi.org/10.1111/j.1467-9574.1994.tb01443.x.

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21

HAPPEL, MARK D., and PETER BOCK. "OVERRIDING THE EXPERTS: A FUSION METHOD FOR COMBINING MARGINAL CLASSIFIERS." International Journal on Artificial Intelligence Tools 10, no. 01n02 (March 2001): 157–79. http://dx.doi.org/10.1142/s0218213001000465.

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The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classification decisions from marginal density functions using an additional classifier. Unlike voting methods, this method can select a more appropriate class than the ones selected by the marginal classifiers, thus "overriding" their decisions. It is shown that this method always exhibits an asymptotic probability of error no worse than the probability of error of the best marginal classifier.

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22

McCullagh, Peter. "Marginal likelihood for parallel series." Bernoulli 14, no. 3 (August 2008): 593–603. http://dx.doi.org/10.3150/07-bej119.

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23

Ando, Shuji. "Asymmetry Model Using Marginal Ridits for Ordinal Square Contingency Tables." Austrian Journal of Statistics 51, no. 1 (January 24, 2022): 70–82. http://dx.doi.org/10.17713/ajs.v51i1.1210.

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This study proposes a new marginal asymmetry model which can infer the relation between marginal ridits of row and column variables for ordinal square contingency tables. When the marginal homogeneity model does not hold, we will apply marginal asymmetry models (e.g., the marginal cumulative logistic and extended marginal homogeneity models). On the other hand, we may measure the degree of departure from the marginal homogeneity model. To measure the degree of that, multiple indexes were proposed. Some of them correspond to the marginal cumulative logistic and extended marginal homogeneity models. The proposed model corresponds to the index, which represents the degree of departure from the MH model, using marginal ridits. We compare the proposed model with the existing marginal asymmetry models and show that the proposed model provides better fit performance than them for real data.

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24

Chattopadhyay, Rita, Zheng Wang, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye. "Batch Mode Active Sampling Based on Marginal Probability Distribution Matching." ACM Transactions on Knowledge Discovery from Data 7, no. 3 (September 2013): 1–25. http://dx.doi.org/10.1145/2513092.2513094.

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25

Chau, T. "Marginal maximum entropy partitioning yields asymptotically consistent probability density functions." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 4 (April 2001): 414–17. http://dx.doi.org/10.1109/34.917576.

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26

Imai, Kosuke, and Marc Ratkovic. "Robust Estimation of Inverse Probability Weights for Marginal Structural Models." Journal of the American Statistical Association 110, no. 511 (July 3, 2015): 1013–23. http://dx.doi.org/10.1080/01621459.2014.956872.

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27

Fan, Rocky Y. K., and Christopher A. Field. "Approximations for marginal densities ofM-estimators." Canadian Journal of Statistics 23, no. 2 (June 1995): 185–97. http://dx.doi.org/10.2307/3315444.

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28

Bercu, Bernard, Fabrice Gamboa, and Marc Lavielle. "Estimation of marginal and spectral modes." Journal of Nonparametric Statistics 14, no. 4 (January 2002): 353–66. http://dx.doi.org/10.1080/10485250213108.

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29

Colombi, Roberto, Sabrina Giordano, Anna Gottard, and Maria Iannario. "Hierarchical marginal models with latent uncertainty." Scandinavian Journal of Statistics 46, no. 2 (November 22, 2018): 595–620. http://dx.doi.org/10.1111/sjos.12366.

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30

Kharroubi, Samer A. "Approximate marginal densities of independent parameters." Statistics 46, no. 4 (February 2, 2011): 459–71. http://dx.doi.org/10.1080/02331888.2010.540667.

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31

Du, Chao, Chu-Lan Michael Kao, and S. C. Kou. "Stepwise Signal Extraction via Marginal Likelihood." Journal of the American Statistical Association 111, no. 513 (January 2, 2016): 314–30. http://dx.doi.org/10.1080/01621459.2015.1006365.

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32

Beneš, Viktor. "Marginal problems in stereology." Advances in Applied Probability 30, no. 2 (June 1998): 273–74. http://dx.doi.org/10.1017/s0001867800046991.

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33

Iki, Kiyotaka, Kouji Tahata, and Sadao Tomizawa. "Measure of departure from marginal homogeneity using marginal odds for multi-way tables with ordered categories." Journal of Applied Statistics 39, no. 2 (February 2012): 279–95. http://dx.doi.org/10.1080/02664763.2011.586682.

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34

Fytas, N. G., A. Malakis, and I. A. Hadjiagapiou. "Quenched bond randomness in marginal and non-marginal Ising spin models in 2D." Journal of Statistical Mechanics: Theory and Experiment 2008, no. 11 (November 11, 2008): P11009. http://dx.doi.org/10.1088/1742-5468/2008/11/p11009.

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35

Gunawan, David, Mohamad A. Khaled, and Robert Kohn. "Mixed Marginal Copula Modeling." Journal of Business & Economic Statistics 38, no. 1 (July 11, 2018): 137–47. http://dx.doi.org/10.1080/07350015.2018.1469998.

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36

Steyn, P. W., and D. J. De Waal. "Occupancy distributions for testing marginal homogeneity." Communications in Statistics - Theory and Methods 22, no. 5 (January 1993): 1299–314. http://dx.doi.org/10.1080/03610929308831087.

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37

Lloyd, C. J. "Optimality of marginal likelihood estimating equations." Communications in Statistics - Theory and Methods 16, no. 6 (January 1987): 1733–41. http://dx.doi.org/10.1080/03610928708829467.

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38

Gelfand, Alan E., and Adrian F. M. Smith. "Gibbs sampling for marginal posterior expectations." Communications in Statistics - Theory and Methods 20, no. 5-6 (January 1991): 1747–66. http://dx.doi.org/10.1080/03610929108830595.

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39

Ernest, Jan, and Peter Bühlmann. "Marginal integration for nonparametric causal inference." Electronic Journal of Statistics 9, no. 2 (2015): 3155–94. http://dx.doi.org/10.1214/15-ejs1075.

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40

Sabourin, Anne, and Johan Segers. "Marginal standardization of upper semicontinuous processes. With application to max-stable processes." Journal of Applied Probability 54, no. 3 (September 2017): 773–96. http://dx.doi.org/10.1017/jpr.2017.34.

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Abstract Extreme value theory for random vectors and stochastic processes with continuous trajectories is usually formulated for random objects where the univariate marginal distributions are identical. In the spirit of Sklar's theorem from copula theory, such marginal standardization is carried out by the pointwise probability integral transform. Certain situations, however, call for stochastic models whose trajectories are not continuous but merely upper semicontinuous (USC). Unfortunately, the pointwise application of the probability integral transform to a USC process does not, in general, preserve the upper semicontinuity of the trajectories. In this paper we give sufficient conditions to enable marginal standardization of USC processes and we state a partial extension of Sklar's theorem for USC processes. We specialize the results to max-stable processes whose marginal distributions and normalizing sequences are allowed to vary with the coordinate.

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41

Fong, E., and C. C. Holmes. "On the marginal likelihood and cross-validation." Biometrika 107, no. 2 (January 24, 2020): 489–96. http://dx.doi.org/10.1093/biomet/asz077.

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Summary In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through $k$-fold partitioning or leave-$p$-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-$p$-out crossvalidation averaged over all values of $p$ and all held-out test sets when using the log posterior predictive probability as the scoring rule. Moreover, the log posterior predictive score is the only coherent scoring rule under data exchangeability. This offers new insight into the marginal likelihood and cross-validation, and highlights the potential sensitivity of the marginal likelihood to the choice of the prior. We suggest an alternative approach using cumulative cross-validation following a preparatory training phase. Our work has connections to prequential analysis and intrinsic Bayes factors, but is motivated in a different way.

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42

Ortego, M. I., and J. J. Egozcue. "Bayesian estimation of the orthogonal decomposition of a contingency table." Austrian Journal of Statistics 45, no. 4 (July 28, 2016): 45–56. http://dx.doi.org/10.17713/ajs.v45i4.136.

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In a multinomial sampling, contingency tables can be parametrized by probabilities of each cell. These probabilities constitute the joint probability function of two or more discrete random variables. These probability tables have been previously studied from a compositional point of view. The compositional analysis of probability tables ensures coherence when analysing sub-tables. The main results are:(1) given a probability table, the closest independent probability table is the product of their geometric marginals;(2) the probability table can be orthogonally decomposed into an independent table and an interaction table;(3) the departure of independence can be measured using simplicial deviance, which is the Aitchison square norm of the interaction table.In previous works, the analysis has been performed from a frequentist point of view. This contribution is aimed at providing a Bayesian assessment of the decomposition. The resulting model is a log-linear one, which parameters are the centered log-ratio transformations of the geometric marginals and the interaction table.Using a Dirichlet prior distribution of multinomial probabilities, the posterior distribution of multinomial probabilities is again a Dirichlet distribution. Simulation of this posterior allows to study the distribution of marginal and interaction parameters, checking the independence of the observed contingency table and cell interactions.The results corresponding to a two-way contingency table example are presented.

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43

Agarwal, Anurag, David L. Farnsworth, Carl V. Lutzer, James E. Marengo, and J. A. Stephen Viggiano. "Extremal Correlation Coefficients for Bivariate Probability Distributions with Specified Marginal Distributions." College Mathematics Journal 52, no. 1 (January 1, 2021): 45–53. http://dx.doi.org/10.1080/07468342.2021.1845539.

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44

Valenzuela, Jorge F. "Analytical Approximation to the Probability Distribution of Electricity Marginal Production Costs." Journal of Energy Engineering 131, no. 2 (August 2005): 157–71. http://dx.doi.org/10.1061/(asce)0733-9402(2005)131:2(157).

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45

Carbone, P., and D. Petri. "Marginal probability density function of granular quantization error in uniform quantizers." IEEE Transactions on Information Theory 47, no. 3 (March 2001): 1237–42. http://dx.doi.org/10.1109/18.915696.

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46

Okwonu, F. "Supervised Linear Classification Performance Based on Marginal Probability for Two Groups." British Journal of Mathematics & Computer Science 5, no. 5 (January 10, 2015): 606–12. http://dx.doi.org/10.9734/bjmcs/2015/13449.

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47

Breskin, Alexander, Stephen R. Cole, and Daniel Westreich. "Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models." Epidemiology 29, no. 3 (May 2018): 352–55. http://dx.doi.org/10.1097/ede.0000000000000813.

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48

Logunov, P. L. "On estimates of the probability of domination with fixed marginal distributions." Journal of Soviet Mathematics 59, no. 4 (April 1992): 952–54. http://dx.doi.org/10.1007/bf01099124.

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49

Fraser, D. A. S., and A. C. M. Wong. "Approximate Studentization with marginal and conditional inference." Canadian Journal of Statistics 21, no. 3 (September 1993): 313–20. http://dx.doi.org/10.2307/3315757.

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50

Walter, Warmuth. "Marginal-frechet-bounds for multidimensional distribution functions." Statistics 19, no. 2 (January 1988): 283–94. http://dx.doi.org/10.1080/02331888808802100.

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