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Relevant bibliographies by topics / Probability mass functions

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Academic literature on the topic 'Probability mass functions'

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Published: 6 September 2023

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Journal articles on the topic "Probability mass functions"

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Gilbert, L., G. de Cooman, and E. E. Kerre. "Practical implementation of possibilistic probability mass functions." Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, no. 5 (2003): 304–9. http://dx.doi.org/10.1007/s00500-002-0217-3.

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Racine, Jeffrey S., Qi Li, and Karen X. Yan. "Kernel smoothed probability mass functions for ordered datatypes." Journal of Nonparametric Statistics 32, no. 3 (2020): 563–86. http://dx.doi.org/10.1080/10485252.2020.1759595.

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Drane, J. Wanzer, Suhua Cao, Lixia Wang, and T. Postelnicu. "Limiting Forms of Probability Mass Functions via Recurrence Formulas." American Statistician 47, no. 4 (1993): 269. http://dx.doi.org/10.2307/2685285.

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Drane, J. Wanzer, Suhua Cao, Lixia Wang, and T. Postelnicu. "Limiting forms of Probability Mass Functions via Recurrence Formulas." American Statistician 47, no. 4 (1993): 269–74. http://dx.doi.org/10.1080/00031305.1993.10475996.

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Bergbusch, Peter A. "Isochrone Probability Functions for Old Stellar Systems." Symposium - International Astronomical Union 190 (1999): 345–46. http://dx.doi.org/10.1017/s0074180900118170.

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The isochrone probability function (IPF) is derived from the slope of the mass–distance relation on an isochrone in where the distance along the isochrone is computed with respect to some arbitrary, well-defined point. IPFs contain the information needed to calculate both luminosity functions and color functions, and they provide a straightforward way of generating synthetic stellar populations.

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Provost, Serge B., Min Jiang, and Hyung-Tae Ha. "Moment-Based Approximations of Probability Mass Functions with Applications Involving Order Statistics." Communications in Statistics - Theory and Methods 38, no. 12 (2009): 1969–81. http://dx.doi.org/10.1080/03610920902835052.

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Warr, Richard L. "Numerical Approximation of Probability Mass Functions via the Inverse Discrete Fourier Transform." Methodology and Computing in Applied Probability 16, no. 4 (2013): 1025–38. http://dx.doi.org/10.1007/s11009-013-9366-3.

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Rachev, S. T., and I. Olkin. "Mass transportation problems with capacity constraints." Journal of Applied Probability 36, no. 2 (1999): 433–45. http://dx.doi.org/10.1239/jap/1032374463.

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We exhibit solutions of Monge–Kantorovich mass transportation problems with constraints on the support of the feasible transportation plans and additional capacity restrictions. The Hoeffding–Fréchet inequalities are extended for bivariate distribution functions having fixed marginal distributions and satisfying additional constraints. Sharp bounds for different probabilistic functionals (e.g. Lp-distances, covariances, etc.) are given when the family of joint distribution functions has prescribed marginal distributions, satisfies restrictions on the support, and is bounded from above, or belo

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Wang, Jiandong, Zhen Wang, Xuan Zhou, and Fan Yang. "Design of delay timers based on estimated probability mass functions of alarm durations." Journal of Process Control 110 (February 2022): 154–65. http://dx.doi.org/10.1016/j.jprocont.2022.01.002.

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Belaid, Nawal, Smail Adjabi, Nabil Zougab, and Célestin C. Kokonendji. "Bayesian bandwidth selection in discrete multivariate associated kernel estimators for probability mass functions." Journal of the Korean Statistical Society 45, no. 4 (2016): 557–67. http://dx.doi.org/10.1016/j.jkss.2016.04.001.

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Dissertations / Theses on the topic "Probability mass functions"

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Bayramoglu, Muhammet Fatih. "The Hilbert Space Of Probability Mass Functions And Applications On Probabilistic Inference." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613613/index.pdf.

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The Hilbert space of probability mass functions (pmf) is introduced in this thesis. A factorization method for multivariate pmfs is proposed by using the tools provided by the Hilbert space of pmfs. The resulting factorization is special for two reasons. First, it reveals the algebraic relations between the involved random variables. Second, it determines the conditional independence relations between the random variables. Due to the first property of the resulting factorization, it can be shown that channel decoders can be employed in the solution of probabilistic inference problems other tha

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Kharoufeh, Jeffrey P. "Density estimation for functions of correlated random variables." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177097417.

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Pfister, Mark. "Distribution of a Sum of Random Variables when the Sample Size is a Poisson Distribution." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3459.

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A probability distribution is a statistical function that describes the probability of possible outcomes in an experiment or occurrence. There are many different probability distributions that give the probability of an event happening, given some sample size n. An important question in statistics is to determine the distribution of the sum of independent random variables when the sample size n is fixed. For example, it is known that the sum of n independent Bernoulli random variables with success probability p is a Binomial distribution with parameters n and p: However, this is not true when

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Green, Christopher Lee. "IP Algorithm Applied to Proteomics Data." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd618.pdf.

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Gomez, Beltran Liseth Ana Délia. "A interação entre a fertilidade do solo e a massa da semente influencia os atributos funcionais de espécies arbóreas tropicais?" Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9380.

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Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2017-10-02T13:02:18Z No. of bitstreams: 1 DissLADGB.pdf: 1548410 bytes, checksum: ca54122f2b96c487bc173d22135e8a0f (MD5)<br>Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-02-05T16:59:56Z (GMT) No. of bitstreams: 1 DissLADGB.pdf: 1548410 bytes, checksum: ca54122f2b96c487bc173d22135e8a0f (MD5)<br>Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-02-05T17:00:12Z (GMT) No. of bitstreams: 1 DissLADGB.pdf: 1548410 bytes, checksum: ca54122f2b96

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Somé, Sobom Matthieu. "Estimations non paramétriques par noyaux associés multivariés et applications." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2030/document.

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Dans ce travail, l'approche non-paramétrique par noyaux associés mixtes multivariés est présentée pour les fonctions de densités, de masse de probabilité et de régressions à supports partiellement ou totalement discrets et continus. Pour cela, quelques aspects essentiels des notions d'estimation par noyaux continus (dits classiques) multivariés et par noyaux associés univariés (discrets et continus) sont d'abord rappelés. Les problèmes de supports sont alors révisés ainsi qu'une résolution des effets de bords dans les cas des noyaux associés univariés. Le noyau associé multivarié est ensuite d

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Bailey, Lara. "Deformable Registration to Create Cytoarchitectonic Probability Maps for Functional Analysis of Primary Auditory Cortex." Thesis, 2008. http://hdl.handle.net/1974/1536.

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A novel method is presented for analyzing fMRI data, which relies on probabilistic estimates of microanatomically defined regions in individual fMRI volunteers. Postmortem structural and cytoarchitectonic information from the Julich/Dusseldorf group in Germany is aligned to the high-resolution structural MR images of functional MRI volunteers. This is achieved using nonlinear registration, which is applied only to the region of interest. The registered postmortem datasets are then combined into probability maps for microanatomically defined regions that are tailored to the anatomy of individua

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Books on the topic "Probability mass functions"

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Sharma, Megha. Computing the probability mass function of the maximum flow through a reliable network. Indian Institute of Management, 2009.

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Schurz, Henri, Philip J. Feinsilver, Gregory Budzban, and Harry Randolph Hughes. Probability on algebraic and geometric structures: International research conference in honor of Philip Feinsilver, Salah-Eldin A. Mohammed, and Arunava Mukherjea, June 5-7, 2014, Southern Illinois University, Carbondale, Illinois. Edited by Mohammed Salah-Eldin 1946- and Mukherjea Arunava 1941-. American Mathematical Society, 2016.

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Horing, Norman J. Morgenstern. Retarded Green’s Functions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0005.

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Chapter 5 introduces single-particle retarded Green’s functions, which provide the probability amplitude that a particle created at (x, t) is later annihilated at (x′,t′). Partial Green’s functions, which represent the time development of one (or a few) state(s) that may be understood as localized but are in interaction with a continuum of states, are discussed and applied to chemisorption. Introductions are also made to the Dyson integral equation, T-matrix and the Dirac delta-function potential, with the latter applied to random impurity scattering. The retarded Green’s function in the prese

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Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how

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VanMarcke, E. H. Quantum Origins of Cosmic Structure: Probability Density Function of Quantity-mass-ratio Logarithm. Swets & Zeitlinger Publishers, 1997.

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Information-Theoretic Methods for Estimating of Complicated Probability Distributions, Volume 207 (Mathematics in Science and Engineering). Elsevier Science, 2006.

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Book chapters on the topic "Probability mass functions"

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Kay, Steven M. "Conditional Probability Mass Functions." In Intuitive Probability and Random Processes Using MATLAB®. Springer US, 2012. http://dx.doi.org/10.1007/0-387-24158-2_8.

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Yi, Pu, Hao Wang, Tao Xie, Darko Marinov, and Wing Lam. "A Theoretical Analysis of Random Regression Test Prioritization." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_12.

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AbstractRegression testing is an important activity to check software changes by running the tests in a test suite to inform the developers whether the changes lead to test failures. Regression test prioritization (RTP) aims to inform the developers faster by ordering the test suite so that tests likely to fail are run earlier. Many RTP techniques have been proposed and are often compared with the random RTP baseline by sampling some of the n! different test-suite orders for a test suite with n tests. However, there is no theoretical analysis of random RTP. We present such an analysis, deriving probability mass functions and expected values for metrics and scenarios commonly used in RTP research. Using our analysis, we revisit some of the most highly cited RTP papers and find that some presented results may be due to insufficient sampling. Future RTP research can leverage our analysis and need not use random sampling but can use our simple formulas or algorithms to more precisely compare with random RTP.

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Kutílek, Miroslav, Miroslav Krejca, and Jana Kupcová-Vlašimská. "The role of estimation error in probability density function of soil hydraulic parameters: Pedotop scale." In Environmental Mechanics: Water, Mass and Energy Transfer in the Biosphere. American Geophysical Union, 2002. http://dx.doi.org/10.1029/129gm21.

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Jenny, Patrick, and Daniel W. Meyer. "Transported Probability and Mass Density Function (PDF/MDF) Methods for Uncertainty Assessment and Multi-Scale Problems." In Lecture Notes in Computational Science and Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22061-6_2.

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Liu, Hang, Yuyin Ma, Peng Chen, et al. "Scheduling Multi-workflows over Edge Computing Resources with Time-Varying Performance, A Novel Probability-Mass Function and DQN-Based Approach." In Web Services – ICWS 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59618-7_13.

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Csenki, Attila. "The Number of Visits to a Subset of the State Space by an Irreducible Semi-Markov Process during a Finite Time Interval: The Probability Mass Function." In Dependability for Systems with a Partitioned State Space. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2674-1_10.

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Donovan, Therese M., and Ruth M. Mickey. "Probability Mass Functions." In Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.003.0008.

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This chapter focuses on probability mass functions. One of the primary uses of Bayesian inference is to estimate parameters. To do so, it is necessary to first build a good understanding of probability distributions. This chapter introduces the idea of a random variable and presents general concepts associated with probability distributions for discrete random variables. It starts off by discussing the concept of a function and goes on to describe how a random variable is a type of function. The binomial distribution and the Bernoulli distribution are then used as examples of the probability mass functions (pmf’s). The pmfs can be used to specify prior distributions, likelihoods, likelihood profiles and/or posterior distributions in Bayesian inference.

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Trappenberg, Thomas P. "Basic probability theory." In Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.003.0006.

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The discussion provides a refresher of probability theory, in particular with respect to the formulations that build the theoretical language of modern machine learning. Probability theory is the formalism of random numbers, and this chapter outlines what these are and how they are characterized by probability density or probability mass functions. How such functions have traditionally been characterized is covered, and a review of how to work with such mathematical objects such as transforming density functions and how to measure differences between density function is presented. Definitions and basic operations with multiple random variables, including the Bayes law, are covered. The chapter ends with an outline of some important approximation techniques of so-called Monte Carlo methods.

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Edge, M. D. "Probability and random variables." In Statistical Thinking from Scratch. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827627.003.0005.

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This chapter considers the rules of probability. Probabilities are non-negative, they sum to one, and the probability that either of two mutually exclusive events occurs is the sum of the probability of the two events. Two events are said to be independent if the probability that they both occur is the product of the probabilities that each event occurs. Bayes’ theorem is used to update probabilities on the basis of new information, and it is shown that the conditional probabilities P(A|B) and P(B|A) are not the same. Finally, the chapter discusses ways in which distributions of random variables can be described, using probability mass functions for discrete random variables and probability density functions for continuous random variables.

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Essington, Timothy E. "Random Variables and Probability." In Introduction to Quantitative Ecology. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192843470.003.0007.

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The chapter “Random Variables and Probability” serves as both a review and a reference on probability. The random variable is the core concept in understanding probability, parameter estimation, and model selection. This chapter reviews the basic idea of a random variable and discusses the two main kinds of random variables: discrete random variables and continuous random variables. It covers the distinction between discrete and continuous random variables and outlines the most common probability mass or density functions used in ecology. Advanced sections cover distributions such as the gamma distribution, Student’s t-distribution, the beta distribution, the beta-binomial distribution, and zero-inflated models.

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Conference papers on the topic "Probability mass functions"

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Tan, Choon Peng, Sin Yen Chu, and Wei Yeing Pan. "Finite-order universal portfolios generated by probability mass functions." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICoMEIA 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4915655.

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Bayramoglu, Muhammet Fatih, and Ali Ozgur Yilmaz. "Factorization of joint probability mass functions into parity check interactions." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5205262.

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Manickam, Shivkumar K. "Probability Mass Functions for which Sources have the Maximum Minimum Expected Length." In 2019 National Conference on Communications (NCC). IEEE, 2019. http://dx.doi.org/10.1109/ncc.2019.8732264.

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Li, Hewenxuan, and David Chelidze. "Identification of Variable Amplitude Fatigue Loading Based on Bivariate Probability Mass Functions." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97488.

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Abstract Fatigue life estimation under variable amplitude (VA) loading remains one of the major unresolved engineering problems. When engineering structures experience VA loading, the corresponding load at the crack front will be highly non-stationary. Under such conditions, the load interaction effects greatly affect fatigue crack propagation rate and can cause significant variation in the corresponding fatigue life. Existing characterization methods focus on single overload effect and their extensions to sequence of overloads which does not reflect the irregularity of the realistic loading.

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Fatih Bayramoglu, Muhammet, and Ali Ozgur Yilmaz. "A Hilbert space of probability mass functions and applications on the sum-product algorithm." In 2008 5th International Symposium on Turbo Codes and Related Topics. IEEE, 2008. http://dx.doi.org/10.1109/turbocoding.2008.4658722.

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Wong, Chun Nam, Hong-Zhong Huang, Jingqi Xiong, and Tianyou Hu. "Weibull Distributed Stress-Dependent Strength Analysis of Aeroengine Alloy Using Lagrange Factor Polynomial." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28090.

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In this paper, the unilateral dependency of strength on stress is taken into account. And the stress-dependent strength is represented by a discrete random variable that has different conditional probability mass functions under different stress amplitudes. Then the Lagrange factor polynomial technique is developed to generate the stress-strength interference model with stress-dependent strength. This model assumes that the strength probability mass function is Weibull distributed, while the stress probability mass function is Normal distributed. Accuracy of this method is investigated by an a

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Jeong, D. Y. "Probabilistic Approach to Conditional Probability of Release of Hazardous Materials From Railroad Tank Cars During Accidents." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10872.

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This paper describes a probabilistic approach to estimate the conditional probability of release of hazardous materials from railroad tank cars during train accidents. Monte Carlo methods are used in developing a probabilistic model to simulate head impacts. The model is based on the physics of impact in conjunction with assumptions regarding the probability distribution functions of the various factors that affect the loss of lading. These factors include impact velocity, indenter size, tank material, tank diameter, effective collision mass, and tank thickness. Moreover, each factor is treate

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Ward, Nicholas J., Stephen Ekwaro-Osire, and João Paulo Dias. "Uncertainty Quantification of Mass and Aerodynamic Rotor Imbalance for Offshore Wind Turbines." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15792.

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Abstract One promising way to address turbine durability issues is early detection of mass and aerodynamic imbalances. More probabilistic methods are necessary to improve the accuracy of rotor imbalance diagnostics. The research question that this work addresses is: can current imbalance detection for an offshore wind turbine be improved through uncertainty quantification of its operating conditions? An uncertainty quantification strategy was proposed to model uncertainties in wind speed, pitch angle, and blade mass density using assumed probability density functions based on available data/in

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Yin, Shih-Hsun, and Bogdan I. Epureanu. "High-Sensitivity Mass Sensing Based on Enhanced Nonlinear Dynamics and Attractor Morphing Modes." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15648.

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This paper demonstrates two novel methods for identifying small parametric variations in an experimental system based on the analysis of sensitivity vector fields (SVFs) and probability density functions (PDFs). The experimental system includes a smart sensing beam excited by a nonlinear feedback excitation through two PZT (lead zirconate titanate) patches symmetrically bonded on both sides at the root of the beam. The nonlinear feedback excitation requires the measurement of the dynamics (e.g. velocity of one point at the tip of the beam) and a nonlinear feedback loop, and is designed such th

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Nieckele, Angela O., Mônica F. Naccache, Marcos S. P. Gomes, João E. Carneiro, and Ricardo Serfaty. "Models Evaluations of Combustion Process in a Cylindrical Furnace." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/htd-24232.

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Abstract In this work an evaluation of different combustion models for predicting oxygen enriched combustion processes was performed. Two types of models were selected. The first one was a generalized finite rate model, in which the conservation equation for the mass concentration was solved, for all species present in the process. In this modeling approach, three different reaction rate expressions were considered. The second case was based on the PDF formulation, which consisted in solving the conservation equations for the mass fraction and its variance. In this second approach the species

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Reports on the topic "Probability mass functions"

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider diseas

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