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 Probability:     more books (100)

161. Calculus And Probability
Online course by Stefan Waner and Steven R. Costenoble
http://people.hofstra.edu/faculty/Stefan_Waner/cprob/cprobintro.html
A Complete Text Resource on the World Wide Web by Stefan Waner and Steven R. Costenoble
Table of Contents 1. Continuous Random Variables and Histograms Exercises 2. Probability Density Functions: Uniform, Exponential, Normal, and Beta Exercises 3. Mean, Median, Variance and Standard Deviation Exercises 4. You're the Expert Creating a Family Trust Back to Main Page Introduction Y ou are a financial planning consultant at a neighborhood bank. A 22-year-old client asks you the following question: "I would like to set up my own insurance policy by opening a trust account into which I can make monthly payments starting now, so that upon my death or my ninety-fifth birthday - whichever comes sooner - the trust can be expected to be worth \$500,000. How much should I invest each month?" Assuming a 5% rate of return on investments, how should you respond? To answer the question on the previous page, we must know something about the probability of the client's dying at various ages. There are so many possible ages to consider (particularly since we should consider the possibilities month by month) that it would be easier to treat his age at death as a continuous variable, one that can take on any real value (between 22 and 95 in this case). The mathematics needed to do probability and statistics with continuous variables is calculus. The material on statistics in this resource will be readily accessible to you if you are familar with derivatives and integrals (through Chapter 6 of

162. Theory Of Probability And Mathematical Statistics
Available in print format, Theory of probability and Mathematical Statistics Theory of probability and Mathematical Statistics
http://www.ams.org/journals/tpms/
 ISSN: 1547-7371(e) 1061-0022(p) Recently posted articles Most recent issue All issues About this journal Subscription information Comments: webmaster@ams.org Privacy Statement Search the AMS

163. 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
 The Math Forum discussions have moved. Math Forum Discussions Please update your bookmarks.

164. Http://www.i-journals.org/ps
The URL of probability Surveys has changed. Please visit http//www.ijournals.org/ps.
http://www.vtex.lt/ejournals/ps/
 The URL of Probability Surveys has changed. Please visit http://www.i-journals.org/ps

165. Probability Distributions
Modeling the probability of success for a binomial distribution. Technical Details Open applet Top of page. The F Distribution is used in many situations,
http://www.stat.vt.edu/~sundar/java/applets/Distributions.html
Probability Distributions
Distributions:
• Binomial Distribution Poisson Distribution Negative Binomial Distribution Geometric Distribution ... F Distribution
• Specialty Functions
• Gamma Function Beta Function The Binomial Distribution is used in finite sampling problems where each observation is one of two possible outcomes ("success" or "failure"). The binomial distribution has two parameters:
• n = the sample size, and = P("success").
• Example: To assure quality of a product, a random sample of size 25 is drawn from a process. The number of defects ( X ) found in the sample is recorded. The random variable X follows a binomial distribution with n = 25 and = P(product is defective). Technical Details
Open applet

Top of page
The Poisson Distribution is used for modeling rates of occurrence. The Poisson distribution has one parameter:
• = the rate (mean).
• Example: A process that creates fabric is monitored. If the number of defects ( X ) per meter of fabric exceeds 5 then the process is stopped for diagnosis. The random variable X follows a Poisson distribution with = number of defects per meter of fabric.

166. Olivers Homepage
A comprehensive course in probability by Oliver Knill
http://www.math.harvard.edu/~knill/index.html
Olivers Homepage
Oliver Knill
Harvard University

One Oxford Street
Cambridge, MA 02138, USA Department of Mathematics
Office: SciCen 434 (Cam)
Tel: (617) 495 5549
Email: document.write(String.fromCharCode(107,110,105,108,108)) .harvard.edu
Vita Math Teaching Etc
Search math.harvard.edu: Last upload of entire page: Thu Sep 15 22:37:48 EDT 2005

 167. Weather Underground: US 12Hr probability of Precip. Ending 800 AM EDT August 10, 2005 Dew Point Prevalent Weather; 12Hr probability of Precip. Sky Coverhttp://www.wunderground.com/ndfdimage/viewimage?type=pop12®ion=us

168. "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/
 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.

169. Probability
probability provides a mathematical description of randomness. The mathematical study of randomness is called probability theory.
http://www.stat.tamu.edu/stat30x/notes/node51.html
Next: Experiments and Events Up: ProbabilityRandom Variables, and Previous: ProbabilityRandom Variables, and
Probability
Probability provides a mathematical description of randomness. A phenomenon is called random if the outcome of an experiment is uncertain. However, random phenomena often follow recognizable patterns. This long-run regularity of random phenomena can be described mathematically. The mathematical study of randomness is called probability theory

Jan Lethen
Wed Nov 13 16:20:46 CST 1996

170. Rudolf Carnap, The Function And Design Of Explications; & Boardman's Notes.
Excerpt from the 1950 paper by Carnap.
http://www.lawrence.edu/fac/boardmaw/Carnap_Explica2.html
[reposted March 24, 1997] Boardman's Notes: The Criteria of Adequacy of An Explication, illustrated by a discussion of Ayer's Language, Truth and Logic [Original pagination is shown in square brackets;
except for this, comments surrounded by brackets are Carnap's.] FROM pages 3-15 of:
by RUDOLF CARNAP
[p. 3:] § 2. On the Clarification of an Explicandum By the procedure of explication we mean the transformation of an inexact, prescientific concept, the explicandum, into a new exact concept, the explicatum. Although the explicandum cannot be given in exact terms, it should be made as clear as possible by informal explanations and examples. The task of explication consists in transforming a given more or less inexact concept into an exact one or, rather, in replacing the first by the second. We call the given concept (or the term used for it) the explicandum , and the exact concept proposed to take the place of the first (or the term proposed for it) the explicatum . The explicandum may belong to everyday language or to a previous stage in the development of scientific language. The

 171. Center For Applied Probability Center for Applied probabilityColumbia University in the City of New York.http://www.cap.columbia.edu/

172. Let's Make A Deal Math: Probability
What is the probability of winning? The purpose of this WebQuest is to introduce you to Design a game of probability using either a spinner or dice.
http://www.cyberbee.com/probability/mathprob.html
 Linda D. Resch Introduction Task Process Resources ... Conclusion Introduction "Heads, you go first; tails, I do." "I need to roll a seven to win." "If I can spin and land on a green slot the jackpot is mine." How many times have you played a game and hoped to win? What is the probability of winning? The purpose of this WebQuest is to introduce you to some interesting problems and learn about probable outcomes. At the conclusion of the WebQuest you will be able to explain why some choices are better than others. Benchmarks: Grade 6 Strand 8: Data Analysis and Probability SLC 24: Make predictions of outcomes of experiments based upon theoretical probabilitities and explain actual outcomes. Strand 4: Data Analysis and Probability SLC 14: Represent and interpret the possible outcomes for a mathematical situation and calculate probabilities. Task Your task is to solve and analyze several problems about probability. After you have completed this task and understand how probability works, design a game or activity that will show others what you have learned. Your final project will include: A tally sheet showing the results of coin flipping A printed copy showing your results while playing one of the Monty Hall simulation games An explanation about whether or not you should switch your door selection during the Monty Hall game A probability game Answers to the Skill Level and Concept Level questions Skill Level What is the definition of probability?

173. Numeracy In The News - Overview Of Site
Features sections on data collection and sampling, creating graphs, probability and inference. Also, includes special topic articles and teacher spreadsheet programs.
http://ink.news.com.au/mercury/mathguys/mercindx.htm
Articles by Year
Also visit the more general INK site
Last updated: March 2002

174. Statistics Glossary - Probability
The probability of the event can be defined as the limiting value of the relative The addition rule is a result used to determine the probability that
http://www.cas.lancs.ac.uk/glossary_v1.1/prob.html
Contents
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:

 175. Probability Theory In Linguistics II Integrated Frameworks Symposium at the LSA meeting in Atlanta, Georgia. Includes slides, handouts and abstracts.http://turing.wins.uva.nl/~rens/lsa03.html

176. PROBABILITY THEORY -- THE LOGIC OF SCIENCE
Online textbook by E.T. Jaynes in PS format.
http://omega.albany.edu:8008/JaynesBook
PROBABILITY THEORY: THE LOGIC OF SCIENCE
by
E. T. Jaynes

Wayman Crow Professor of Physics
Washington University
St. Louis, MO 63130, U.S.A.
Dedicated to the Memory of Sir Harold Jeffreys, who saw the truth and preserved it. Fragmentary Edition of June 1994 First three chapters in DJVU
PART A - PRINCIPLES AND ELEMENTARY APPLICATIONS
Chapter 1 Plausible Reasoning Chapter 2 Quantitative Rules: The Cox Theorems Fig. 2-1 Chapter 3 Elementary Sampling Theory ... Chapter 18 The A Distribution and Rule of Succession
Chapter 19 Physical Measurements Chapter 20 Regression and Linear Models Chapter 21 Estimation with Cauchy and tDistributions Chapter 22 Time Series Analysis and Autoregressive Models ... Chapter 30 Conclusions
APPENDICES
Appendix A Other Approaches to Probability Theory Appendix B Formalities and Mathematical Style Appendix C Convolutions and Cumulants Appendix D Dirichlet Integrals and Generating Functions Appendix E The Binomial Gaussian Hierarchy of Distributions Appendix F Fourier Analysis Appendix G Infinite Series Appendix H Matrix Analysis and Computation Appendix I Computer Programs
REFERENCES
List of references
To transfer all the chapters (Adobe's pdf format) at once from bayes.wustl.edu

177. Statistics And Probability Letters
Contents and abstracts from vol.41 (1999).
http://www.elsevier.com/inca/publications/store/5/0/5/5/7/3/
 Home Site map Regional Sites Advanced Product Search ... Products Journal information Product description Editorial board Audience Abstracting/indexing ... Special issues and supplements For Authors Guide for authors Subscription information Bibliographic and ordering information Conditions of sale Dispatch dates Journal related information Contact the publisher Impact factor Most downloaded articles Other journals in same subject area ... Select your view Editor: R.A. Johnson See editorial board for all editors information Feedback We are always interested in receiving suggestions on the journal. Also, if you have publication proposals for books or new journals in the field or suggestions for any other kind of cooperation, please do not hesitate to contact us. Sweitze Roffel, Senior Publishing Editor, E-mail: S.Roffel@elsevier.com New related journal: Statistical Methodology ContentsDirect is Elsevier's free e-mail alerting service which delivers content information to subscribers' desktops. Sign up to receive an alert each time an issue of Statistical Methodology becomes available.

 178. Probability Theory In Linguistics Workshop held in Washington, DC, by the Linguistic Society of America, covering probabilistic approaches to a number of subfields. Handouts available in PDF format.http://turing.wins.uva.nl/~rens/lsa.html

179. 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
AutoClass C - General Information
Contents
What Is AutoClass AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features:
• determines the number of classes automatically;
• can use mixed discrete and real valued data;
• can handle missing values;
• processing time is roughly linear in the amount of the data;
• cases have probabilistic class membership;
• allows correlation between attributes within a class;
• generates reports describing the classes found; and
• predicts "test" case class memberships from a "training" classification.
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.

180. Www.math.uah.edu/stat/objects/index.xhtml

http://www.math.uah.edu/stat/objects/index.xhtml
var baseURL = "../"; Virtual Laboratories