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.

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

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.

Excerpt: Digital line graph (DLG) data are digital representations of cartographic information. DLGâ€™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.

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...

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

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.

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...

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...

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...

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).