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Probability Distribution (256 Books)


In probability theory and statistics, a probability distribution identifies either the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous). The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range.

 
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A Continuous Limit for the Contagious Binomial Distribution

By: Montgomery, David Bruce

Bibliography: Leaf [11]

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Multinormal Bayesian analysis; two examples

By: Martin, J. J. (James John)

Supplemental catalog subcollection information: American Libraries Collection; American University Library Collection; Historical Literature; Bibliography: leaf 39

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Bayesian Analysis of Reduced Form Systems

By: Ando, Albert; Kaufman, G. M

Bibliography: Leaf 20

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A Fortiori Bayesian Inference in Psychological Research

By: Lavin, Milton L

Bibliography: l. 54

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Multinormal Bayesian Analysis; Two Examples

By: Martin, J. J. (James John)
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A Bayesian Analysis of Model Specification Uncertainty in Forecast...

By: Benson, Paul George, 1946

Evergreens

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Probability distributions for thunderstorm activity at Cape Kenned...

By: Carter, M. C. ; Williford, W. O; Falls, L. W.

Supplemental catalog subcollection information: NASA Publication Collection; Astrophysics and Technical Documents; Probability distributions for thunderstorms at Cape Kennedy

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On sufficient and necessary statistics for a family of probability...

By: Dynkin, E. B

Supplemental catalog subcollection information: NASA Publication Collection; Astrophysics and Technical Documents

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Usgs Geodata Digital Line Graphs

By: Environmental Protection Agency

Excerpt: 1. Use this form to order U.S. Geological Survey (USGS) digital line graph data. 2. Domestic Orders. Payment (check, money order, purchase order, major credit card, or Government account) must accompany order. Please include a $5 handling fee. Make all drafts payable to the Department of the Interior-USGS. Do not send cash. Delivery will be provided by a Government-selected courier. If you request a specific courier service, please provide your courier account number.

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Us Geodata Digital Line Graphs

By: Environmental Protection Agency

Excerpt: Digital line graph (DLG) data are digital representations of cartographic information. DLG’s of map features are digital vectors converted from maps and related sources. The U.S. Geological Survey (USGS) DLG data are classified as large, intermediate, and small scale.

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Chapter 1 Discrete Probability Distributions

Mathematics document containing theorems and formulas.

Excerpt: In this chapter, we shall first consider chance experiments with a finite number of possible outcomes !1, !2, . . . , !n. For example, we roll a die and the possible outcomes are 1, 2, 3, 4, 5, 6 corresponding to the side that turns up. We toss a coin with possible outcomes H (heads) and T (tails). It is frequently useful to be able to refer to an outcome of an experiment. For example, we might want to write the mathematical expression which gives the sum of fou...

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On the limiting form of the contagious binomial distribution and i...

By: Montgomery, David Bruce

Supplemental catalog subcollection information: American Libraries Collection; American University Library Collection; Historical Literature; Bibliography: leaf [14]

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On the Limiting Form of the Contagious Binomial Distribution and I...

By: Montgomery, David Bruce
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Bayesian Modeling of Nonstationarity in Normal and Lognormal Proce...

By: Velez Arocho, Jorge Ivan, 1947

Civilization

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AutoClass: A Bayesian classification system

By: Cheeseman, Peter; Self, Matthew; Stutz, John; Taylor, Will; Freeman, Do; Kelly, James

Supplemental catalog subcollection information: NASA Publication Collection; Astrophysics and Technical Documents; A program, AutoClass 2, for automatically discovering (inducing) classes from a database is described that is based on a Bayesian statistica

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Bayesian Hierarchical Modeling of U. S. County Poverty Rates

By: Robin Fisher

Statistical Reference Document

Excerpt: The U.S. government, thoutgh the Departments of Education, Health and Human Services, Housing and Urban Development, and Labor, allocates approximately $30 billion in funds annually to programs to aid economically disadvantaged areas of the United States.

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Methods for Record Linkage and Bayesian Networks

By: William E. Winkler

Statistical Reference Document

Introduction: Record linkage is the science of finding matches or duplicates within or across files. Matches are typically delineated using name, address, and date-of-birth information. Other identifiers such as income, education, and credit information might be used. With a pair of records, identifiers might not correspond exactly. For instance, income in one record might be compared to mortgage payment size using a crude regression function. In the computer science lit...

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Bayesian Networks Representations, Generalized Imputation, And Syn...

By: Yves Thibaudeau

Statistical Reference Document

Introduction: Graphical representation of Bayes Nets and other probabilistic relationships date to Lauritzen and Spiegelhalter (1988). They are used extensively in machine learning. For instance, Figure 2 in Getoor et al. (2001) (reprinted below) demonstrates an efficient representation of Census data. 951 parameters are able to represent a potentially large number of cells in a contingency table (7 billion). Bayes Net software will quickly determine dependency relations...

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Methods for Record Linkage and Bayesian Networks

By: William E. Winkler

Statistical Reference Document

Excerpt: Record linkage is the science of finding matches or duplicates within or across files. Matches are typically delineated using name, address, and date-of-birth information. Other identifiers such as income, education, and credit information might be used. With a pair of records, identifiers might not correspond exactly. For instance, income in one record might be compared to mortgage payment size using a crude regression function. In the computer science literatu...

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Bayesian Networks Representations, Generalized Imputation, And Syn...

By: U.S. Census Bureau Department

Statistical Reference Document

Excerpt: Graphical representation of Bayes Nets and other probabilistic relationships date to Lauritzen and Spiegelhalter (1988). They are used extensively in machine learning. For instance, Figure 2 in Getoor et al. (2001) (reprinted below) demonstrates an efficient representation of Census data. 951 parameters are able to represent a potentially large number of cells in a contingency table (7 billion).

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