Extractions: Outcome Sample Space Event Relative Frequency ... Back to Main Contents Outcome An outcome is the result of an experiment or other situation involving uncertainty. The set of all possible outcomes of a probability experiment is called a sample space. Sample Space The sample space is an exhaustive list of all the possible outcomes of an experiment. Each possible result of such a study is represented by one and only one point in the sample space, which is usually denoted by S. Examples 1. Experiment Rolling a die once: 2. Experiment Tossing a coin: 3. Experiment Measuring the height (cms) of a girl on her first day at school: Sample space S = the set of all possible real numbers Event An event is any collection of outcomes of an experiment. Formally, any subset of the sample space is an event. Any event which consists of a single outcome in the sample space is called an elementary or simple event. Events which consist of more than one outcome are called compound events. Set theory is used to represent relationships among events. In general, if A and B are two events in the sample space S, then:
A Short History Of Probability A Short History of probability. Before Laplace, probability theory was solelyconcerned with developing a mathematical analysis of games of chance. http://www.cc.gatech.edu/classes/cs6751_97_winter/Topics/stat-meas/probHist.html
Extractions: From Calculus, Volume II by Tom M. Apostol nd The Dutch scientist Christian Huygens, a teacher of Leibniz, learned of this correspondence and shortly thereafter (in 1657) published the first book on probability; entitled De Ratiociniis in Ludo Aleae , it was a treatise on problems associated with gambling. Because of the inherent appeal of games of chance, probability theory soon became popular, and the subject developed rapidly during the 18th century. The major contributors during this period were Jakob Bernoulli (1654-1705) and Abraham de Moivre (1667-1754). In 1812 Pierre de Laplace (1749-1827) introduced a host of new ideas and mathematical techniques in his book, . Before Laplace, probability theory was solely concerned with developing a mathematical analysis of games of chance. Laplace applied probabilistic ideas to many scientific and practical problems. The theory of errors, actuarial mathematics, and statistical mechanics are examples of some of the important applications of probability theory developed in the l9th century. Like so many other branches of mathematics, the development of probability theory has been stimulated by the variety of its applications. Conversely, each advance in the theory has enlarged the scope of its influence. Mathematical statistics is one important branch of applied probability; other applications occur in such widely different fields as genetics, psychology, economics, and engineering. Many workers have contributed to the theory since Laplace's time; among the most important are Chebyshev, Markov, von Mises, and Kolmogorov.
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New URL For Applied Probability Society Applied probability Society of INFORMS. Our web site has moved to the URLbelow. Please update your links. http//appliedprob.society.informs.org/. http://www.fsa.ulaval.ca/informs/ap/
AutoClass C - General Information An unsupervised Bayesian classification system that seeks a maximum posterior probability classification. http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/autoclass-c-program
Extractions: Information on SNOB (a related classification program) Bayesian Learning Group What Is AutoClass AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features: AutoClass uses only vector valued data, in which each instance to be classified is represented by a vector of values, each value characterizing some attribute of the instance. Values can be either real numbers, normally representing a measurement of the attribute, or they can be discrete, one of a countable attribute dependent set of such values, normally representing some aspect of the attribute.
SpringerLink - Publication Infinite Dimensional Analysis, Quantum probability and Related of mathematicians, mathematical physicists and other scientists who have been drawninto the fields of infinite dimensional analysis and quantum probability. http://www.springerlink.com/openurl.asp?genre=journal&issn=0178-8051
Www.levering.k12.pa.us/WWW/probability/ The probability Pipe OrganThe probability Pipe Organ. Sorryit appears your browser doesn t understandJava applets. probability and Statistics. Table of Contents. by John Walker. http://www.levering.k12.pa.us/WWW/probability/
Math Forum: Search Sci.stat.math Teachers and students of probability and statistics will find others who share their interests at the sci.stat.math discussion list. http://mathforum.org/discussions/epi-search/sci.stat.math.html
Math Project Ideas Site intended for teachers includes project ideas in probability and algebra, genetics, reliability theory, linefitting, and linear algebra. http://www.columbia.edu/~umk1
Probability Explorer Home Page probability Explorer (Stohl, 19992002) is a research-based software applicationdesigned with tools that enable students and teachers to design, simulate, and http://www.probexplorer.com/
Extractions: Probability Explorer (Stohl, 1999-2002) is a research-based software application designed with tools that enable students and teachers to design, simulate, and analyze a variety of probabilistic situations. The software environment can be used for activities from upper elementary grades through high school. Probability Explorer is purposefully designed as an open-ended learning environment with multiple ways to represent data that engage students in designing, simulating, and analyzing results of probability experiments. At a fundamental level, data is represented in PE with randomly generated icons that can be sorted, stacked (in a pictograph) or lined up in the sequence in which they occurred. A Pie Graph (relative frequency), Bar Graph (frequency), and Data Table (counts, fractions, decimals, and percents) are also available to display results in both static form as well as dynamically changing during a simulation. In April 2000
Shih Modelling tool that analyzes data generating classification, regression or class probability prediction models. http://www.shih.be/
Extractions: In insurance companies we assess the risk of a new client. In broker companies we do technical analysis to predict the future tendency of a stock based on technical information. In telephone companies we predict what clients are likely to stay or leave through churn scores. In marketing we segment and profile databases. In the medical field we offer applications for diagnosis and cancer prediction.
Extractions: SticiGui Probability Calculator You need Java to see this. This tool lets you calculate the probability that a random variable X is in a specified range, for a variety of probability distributions for X: the normal distribution , the binomial distribution with parameters n and p , the chi-square distribution , the exponential distribution , the geometric distribution , the hypergeometric distribution , the negative binomial distribution , the Poisson distribution , and Student's t -distribution Description of Applet Controls The first choice box lets you select a probability distribution. Depending on the distribution you select, text areas will appear for you to enter the values of the parameters of the distribution. Parameters that are probabilities ( e.g. , the chance of success in each trial for a binomial distribution) can be entered either as decimal numbers between and 1, or as percentages. If you enter a probability as a percentage, be sure to include the percent sign (%) after the number.
Probability Tutorials:Tutorials www.probability.net, Updated 19Apr-2004. clock468x60a3.gif (9867bytes). probability Tutorials, by Noel Vaillant. Tutorials, A B http://www.netcomuk.co.uk/~vaillant/proba
"DAU STAT REFRESHER MODULE" An interactive module covering basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering. Defense Acquisition University. http://cne.gmu.edu/modules/dau/stat/
Extractions: This module is an interactive tutorial which gives a comprehensive view of Probability and Statistics. This interactive module covers basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering. The Probability and Statistics refresher module is intended for use by DAU course participants. for assistance. The weblike arrangement of the tutorial enables the user to browse randomly from topic to topic. A knowledgeable user will be able to skip subtopics or proceed directly to an area of particular interest. CAUTION TO THE NOVICE USER: The weblike form of this tutorial tempts the user to follow each link offered and, thus, lose the thread of the subtopic originally begun. So it is advisable to limit random browsing from topic to topic when first using the tutorial. The subway lines are provided to help the user stay on the topic thread. In addition, note that the browser provides a way to backtrack through the pages seen using the "Back" button. The user is strongly encouraged to return to this page, the "Stat Home Page", from any point in the tutorial to leave a comment for the developers of the tutorial. (Click on "Comments" below.) The "house" button for quick return to this page is found on the subway map and on each individual page.
Statistics Glossary - Probability STEPS Statistics Glossary. probability. The set of all possible outcomesof a probability experiment is called a sample space. Sample Space http://www.stats.gla.ac.uk/steps/glossary/probability.html
Extractions: Event An event is any collection of outcomes of an experiment. Formally, any subset of the sample space is an event. Any event which consists of a single outcome in the sample space is called an elementary or simple event. Events which consist of more than one outcome are called compound events. Set theory is used to represent relationships among events. In general, if A and B are two events in the sample space S, then (A union B) = 'either A or B occurs or both occur' (A intersection B) = 'both A and B occur' (A is a subset of B) = 'if A occurs, so does B'
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