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         Probability:     more books (100)
  1. Foundations of Modern Probability (Probability and Its Applications) by Olav Kallenberg, 2010-11-02
  2. A Course in Probability Theory, Third Edition by Kai Lai Chung, 2000-10-23
  3. Probability, Reliability, and Statistical Methods in Engineering Design by Achintya Haldar, Sankaran Mahadevan, 1999-11-01
  4. Probability, Random Variables, and Random Signal Principles by Peyton Peebles, 2000-07-21
  5. Probability (Graduate Texts in Mathematics) (v. 95) by Albert N. Shiryaev, 1995-12-08
  6. Probability Essentials by Jean Jacod, Philip Protter, 2002-10-28
  7. Understanding Probability: Chance Rules in Everyday Life by Henk Tijms, 2007-08-06
  8. A Treatise on Probability by John Maynard Keynes, 2010-10-14
  9. Probability: An Introduction by Samuel Goldberg, 1987-01-01
  10. Understanding and Calculating the Odds: Probability Theory Basics and Calculus Guide for Beginners, with Applications in Games of Chance and Everyday Life by Catalin Barboianu, 2006-05-21
  11. Introduction to Probability (Dover Books on Advanced Mathematics) by John E. Freund, 1993-05-19
  12. Introduction to Probability Theory by Paul G. Hoel, Sidney C. Port, et all 1972-06-30
  13. Introduction to Probability by Charles M. Grinstead, J. Laurie Snell, 1997-07-01
  14. The Probability Broach by L. Neil Smith, 2001-12-12

81. Probabilistic Causation
Probabilistic Causation designates a group of philosophical theories that aim to characterize the relationship between cause and effect using the tools of probability theory. A primary motivation for the development of such theories is the desire for a theory of causation that does not presuppose physical determinism.
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Probabilistic Causation
ceteris paribus clause more precise. This article traces these developments, as well as recent, related developments in causal modeling. Issues within, and objections to, probabilistic theories of causation will also be discussed.
  • 1. Introduction and Motivation
    1. Introduction and Motivation
    1.1 Regularity Theories
    an object, followed by another, and where all the objects similar to the first, are followed by objects similar to the second Suggested Readings: Hume (1748), especially section VII.
    1.2 Imperfect Regularities
    The first difficulty is that most causes are not invariably followed by their effects. For example, it is widely accepted that smoking is a cause of lung cancer, but it is also recognized that not all smokers develop lung cancer. (Likewise, not all non-smokers are spared the ravages of that disease.) By contrast, the central idea behind probabilistic theories of causation is that causes raise the probability of their effects; an effect may still occur in the absence of a cause or fail to occur in its presence. Thus smoking is a cause of lung cancer, not because all smokers develop lung cancer, but because smokers are

82. Probability: What Are Your Chances?-NCES Students' Classroom
Ever wonder how often an event might happen? Try What Are Your Chances? Gain a little knowledge about probability.
JavaScript is currently turned off on your machine.
"The probable is what usually happens" - Aristotle. What many people refer to as 'good luck' can actually be explained by a little knowledge about probability and statistics. Our dice game allows you to see how increasing or decreasing the number of dice rolls effects an outcome. So give it a try, choose the number of rolls you would like to make … and roll the dice! "It is a truth very certain that when it is not in our power to determine what is true we ought to follow what is most probable" - Descartes Number of Rolls: Try More Activities! Students' Classroom Home Help Site Index ... NCES Home

83. Applied Probability Society Of INFORMS
A subdivision of the Institute for Operations Research and the Management Sciences (INFORMS) concerned with the application of probability theory to systems
This is the home page of Applied Probability Society of INFORMS.
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84. Mathinfo2000
Aims to bring together researchers in theoretical computer science and mathematics. Topics include trees, stochastic processes, large deviations, branching processes, random walks, discrete probability, enumerative and analytical combinatorics, analysis of algorithms, performance evaluation, and combinatorial optimization. Versailles, France, September 1820, 2000.
Colloquium on Mathematics and Computer Science :
Algorithms, Trees, Combinatorics and Probabilities
Call For Papers

September 18-20, 2000
45, avenue des Etats-Unis
78035 Versailles cedex - France

Scientific Committee

Organisation Committee

Electronic mail :
Scope of the Colloquium

Call For Papers
Appel a communications (postscript) Invited papers Sponsors List of accepted papers Scientific program ... Version francaise Important dates : March 15, 2000 : Deadline for submission of papers (10 pages). May 24, 2000 : Decision of the scientific committee June 15, 2000 : Final version of accepted papers Official languages : English and French Registration Accomodation Conference site LAMA

85. The Probability Web: Home
The probability Web is now located at Carleton College and is maintained by Bob Dobrow. Please update links and bookmarks accordingly
THE PROBABILITY WEB The Probability Web is now located at Carleton College and is maintained by Bob Dobrow. Please update links and bookmarks accordingly:

86. The Monty Hall Problem
A probability theory puzzle with solutions. English and German.
[remote] [frederik] [projects] [monty hall]
The Monty Hall Problem
The following problem is taken from a quiz show on TV that really existed (or still exists). It is an interesting topic to discuss in almost any group of people because even the most intelligent often get into trouble, and is (in other languages) referred to as the Goat Problem. New: In compliance with the current domain grabbing hysteria, this page is now also available as, which is easier to memorize - at least for speakers of German.
The Scenario
The quiz show candidate has mastered all the questions. Now it's all or nothing for one last time: He is lead to a room with three doors. Behind one of them there's an expensive sports car; behind the other two there's a goat. (Don't ask me why it's a goat. That's just the way it is.) The candidate chooses one of the doors. But it is not opened; the host (who knows the location of the sports car) opens one of the other doors instead and shows a goat. The rules of the game, which are known to all participants, require the host to do this irrespective of the candidate's initial choice. The candidate is now asked if he wants to stick with the door he chose originally or if he prefers to switch to the other remaining closed door. His goal is the sports car, of course!

87. Index Of /People/opperm
Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
Index of /People/opperm
Name Last modified Size Description ... Welcome.html 08-Feb-2005 11:03 3.7K manfred.gif 08-Feb-2005 11:03 97K Apache/2.0.52 (Debian GNU/Linux) PHP/4.3.9-1 Server at Port 80

88. Växjö Universitet:
Vaxjo University, Sweden; 27 November 1 December 2000.
Bokis Konferenser Mallar Medel att söka ... Programme/Program International Conference: 2000: November 27 - December 1. International Center for Mathematical Modeling
[in physics, technique and cognitive sciences],
Vaxjo University, Sweden, Organizing committee:
L. Accardi (Rome, Italy),
W. De Muynck (Eindhoven, the Netherlands),
T. Hida (Meijo University, Japan),
A. Khrennikov (Växjö University, Sweden),
V. Maximov(Belostok, Poland). Invited Speakers:
S. Albeverio (Bonn, Germany),
H. Atmanspacher (Freiburg, Germany),
L. Ballentine (Burnaby, Canada), J. Bricmont (Belgium), A. Holevo (Moscow, Russia), S. Gudder (Denver,U.S.A.), T. Kolsrud (Stockholm, Sweden), P. Lahti (Turku, Finland), H. Narnhofer (Wien, Austria), V. Serdobolskii (Moscow, Russia), J. Summhammer (Wien, Austria) , O. Viskov (Moscow, Russia), I. Volovich (Moscow, Russia). The main aim of this Conference is to reconsider foundations of probability theory in connection with foundations of physics (quantum as well as statistical). The following problems will be discussed during the Conference: A. Creation of the conventional probability theory (Kolmogorov's axiomatics, 1933).

89. Probability Distributions
A Compendium of Common probability Distrbutions. Common probability Distributions. This Compendium describes distributions appropriate for the modeling
Common Probability Distributions
This Compendium describes distributions appropriate for the modeling of random data. Although similar summaries may be found in textbooks, this reference exhibits some unusual features, viz.,
  • The number of distributions (56) is large, including
    • Continuous distributions (30)
      Symmetric (11)
      Skewed (19)
    • Continuous binary mixtures (17)
    • Discrete distributions (5)
    • Discrete binary mixtures (4)
  • All formulas are shown in their fully-parametrized form, not the standard form.
  • Many of the formulas given are seldom described.
  • Random variate generation is included where feasible.
  • Each (two-page) entry is readily printable in full 600-dpi resolution (see below), and/or
  • The entire file (549K, pdf) may be downloaded and printed to give a complete reference book
    [but please
Data Modeling
Regress+ The latter is a software tool for mathematical modeling and, hence, this Compendium shares the same focus. All of the distributions described here may be used with Regress+ to model empirical data.

90. Probability And Statistics
Java Demos for probability and Statistics. These demos require a Javaenabled browser. Introduction to probability Models Hypergeometric Distribution
These demos require a Java-enabled browser:
This applet was written by Charles Stanton. The author wishes to thank CSUSB for a Promoting Innovative Instruction Award to write these applets.

91. One Tailed Version Of Chebyshev's Inequality - By Henry Bottomley
Chebyshev's inequality with onetailed and unimodal versions, putting statistical limits on the dispersion of probability distributions.
Chebyshev's inequality
and a one-tailed version
Chebyshev's inequality states that for
which is equivalent to for
A one-tailed version of Chebyshev's inequality is that for
i.e. t
which is equivalent to for
Turning inequality into equality
Turning inequality into equality Proof of Chebyshev's inequality Proof of one-tailed version of Chebyshev's inequality ... Discussion and a new page with more thoughts Speculation on unimodal PDFs or go to a Mode-Mean inequality or Mode-Median-Mean relationships or some Statistics Jokes written by Henry Bottomley
Turning Chebyshev's inequality into an equality
P[X=m-k.s] = 1/(2.k ), P[X=m+k.s] = 1/(2.k ), and P[X=m] = 1-1/k
Note E(X)=m and Var (X)=s , sd(X)=s, so for this X k
The equality will in general only be achieved for a symmetric three-valued distribution. If the probabilities are p, 1-2p and p then equality is achieved when k=(2p) . A symmetric two-valued distribution is a special case with k=1. A chart showing this distribution for k=2 is below (return to top)
Turning one-tailed version of Chebyshev's inequality into an equality
becomes P[X=m+s.k] = 1/(1+k

92. Probability
Chapter 4 probability Simple probability Conditional probability probability of A and B probability of A or B Binomial distribution.
Chapter 4: Probability
Other Sites
Analysis Tools
Binomial probabilities

by B. Narasimhan
Instructional Demos
Normal approximation to binomial

by David Lane
Bayes' theorem

by John Pezzullo
Lets make a deal game

by Webster West
Lets make a deal game
by Stat Dept, U. of Illinois Binomial distribution by B. Narasimhan Binomial distribution by Berrie Zielman Normal approximation to binomial by Keith Dear Normal approximation to the binomial by Berrie Zeilman Dice rolling simulation by Charles Stanton Hypergeometric distribution by Charles Stanton Poisson distribution by Charles Stanton Text Introduction to probability probability distributions independence and tree models conditional probability ... Binomial distribution by Keith Dear Probability More probability by P. B. Stark Probability discrete distributions by H. J. Newton, J. H. Carroll, N. Wang, and D. Whiting

93. GCSE Maths Revision Site
Features brief reviews of basic math concepts, from numbers and fractions to probability and statistics. Also, includes formula page and help forum.
Search 16th Sep 2005
GCSE Maths
Home Revision Guides Formula Sheet Specimen Papers
Numbers Decimals Fractions Directed Numbers ... Proportion
Shape and Space
Angles Circle Theorems Loci Shapes ... Transformations
Statistics and Probability
Probability Averages Standard Deviation Sampling ... Histograms
Travel Graphs Gradients Graphs
Factorising Algebraic Fractions Solving Equations Simultaneous Equations ... Functions
Sin, Cos, Tan Pythagoras Sin and Cosine Formulae Bearings ... Congruency
Coursework Practise Questions
GCSE Maths Revision
Matthew Pinkney This site provides printable study notes and help for GCSE maths students, covering intermediate and higher tier topics.
Fast loading, easy to use (no maze of links to get through!) and regularly updated and improved! Search the GCSE Section:
Don't cry over maths alone, try our forum For more advanced topics, consult the A-Level section
If you have seen this site reviewed somewhere, let me know Support this site by buying revision materials via these links: You are visitor number (since Feb 2000): Please read the

94. The Annals Of Applied Probability
The Annals of Applied probability. An Official Journal of the Institute of Mathematical Statistics. Editors. J. Michael Steele, 19911993

95. The Birthday Problem
Find out how likely it is that two people in a class have the same birthday. With an explanation of the math behind this problem and a simulation.
The Birthday Problem: A short lesson in probability.
Last revised 8/24/05
Introduction with simulation
Happy Birthday! There's a birthday in your class today! Or will there be two? How likely is it that two people in your class have the same birthday? Say your class has 28 students. There are a number of ways to approach this problem. The most common is to take a survey and see if it happens that two birthdays fall on the same day. But if it happens in the surveyed class, will it occur in another class with different students? The question of how likely it is for any given class is still unanswered. Another way, is to survey more and more classes to get an idea of how often the match would occur. This can be time consuming and may require a lot of work. But a computer can help out. Below is a simulation of the birthday problem. It takes a little while (about 25 seconds) to respond, but it will generate a random list of birthdays time after time. Choose a number for your class size and do 10 trials with that size.

96. Probability Theory As Extended Logic
A monumental online book by the late ET Jaynes on Bayesian inference. Also has a number of related articles by other authors.
Probability Theory As Extended Logic
Last Modified Edwin T. Jaynes was one of the first people to realize that probability theory, as originated by Laplace, is a generalization of Aristotelian logic that reduces to deductive logic in the special case that our hypotheses are either true or false. This web site has been established to help promote this interpretation of probability theory by distributing articles, books and related material. As Ed Jaynes originated this interpretation of probability theory we have a large selection of his articles, as well as articles by a number of other people who use probability theory in this way:
  • E. T. Jaynes: Amazon here in the US and from Amazon in the UK. We still have a selection of Dr. Jaynes' articles , and the first three chapters from Jaynes' book on probability theory are now online as a pdf file or as a postscript file. A typed publication quality version of his unpublished book titled "Probability Theory, With Applications in Science and Engineering" that was being prepared for publication in the mid 1970's is available.

97. International Conference On Probability And Number Theory, Kanazawa 2005
Kanazawa Yomiuri Kaikan, Japan; 2024 June 2005.
International Conference on
Probability and Number Theory 2005
June 20 (Mon) - June 24 (Fri), 2005
Kanazawa, Japan
The conference is focused on an intersection of probability theory and number theory, with both fields considered in a wide sense. The conference consists of invited talks and poster sessions. Kanazawa is located in the central area in Japan, faced to the Japan Sea, and is regarded as one of the most beautiful cities in Japan.
  • Photos
  • Programme
  • Instruction for participants
  • General information This conference is sponsored by the Japan Society for the Promotion of Science (JSPS).
    More photos in separate web page: Click here.
    page top
    Scientific committee
    Kohji Matsumoto (Nagoya University)
    Hiroshi Sugita (Osaka University)

    Shigeki Akiyama (Niigata University)
    Shunji Ito (Kanazawa University)
    Leo Murata (Meijigakuin University)
    Satoshi Takanobu (Kanazawa University)
    page top
    List of invited speakers
    Adamczewski, B. (CNRS, France)
  • 98. HyperStat Online: Probability
    Web based materials for teaching statistics."HSframes"

    99. Studia Mathematica
    Papers in functional analysis, mathematical analysis and probability theory. Abstracts available without subscription.

    100. Statistics Glossary - Probability
    statistical glossary probability. In probability theory we say that two events, A and B, are independent if the probability that they both occur is
    Outcome Sample Space Event Relative Frequency ... Index of all entries
    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.
    Experiment Rolling a die once:
    Experiment Tossing a coin:
    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 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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