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Computing Conditional Probabilities in Large Domains by
Computing Conditional Probabilities in Large Domains by

Weak Convergence of Probability Measures
Weak Convergence of Probability Measures

Near-Optimal Reinforcement Learning in Polynomial Time
Near-Optimal Reinforcement Learning in Polynomial Time

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slides - John L. Pollock

... The Epistemology of Probability • It is generally supposed that the probability calculus completely characterizes the logical and mathematical structure of probabilities. • It is supposed that familiar sorts of statistical inference provide us with our basic knowledge of probabilities. • Then appea ...
Learnability and the Vapnik
Learnability and the Vapnik

... K. Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension. Tech. Rep. UCSC-CRL-86-5. Univ. Calif. at Santa Cruz, Santa Cruz, CA, 1986. The work of D. Haussler and M. K. Warmuth was supported by the Office of Naval Research grant N00014-86-K-0454. The work of A. Blumer was su ...
Saving Schr¨odinger`s Cat: It`s About Time (not
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... states of affairs has obtained: at t = 60 it is true that either the cat still lives, or it is true that it is deceased. The way that Measure interacts with Truth and State, through Born, has been taken to be highly problematic. At first it seems as if the reverse direction of Truth—if a measurement ...
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Nonparametric Priors on Complete Separable Metric Spaces

XC-BK5 - Eclectic Anthropology Server
XC-BK5 - Eclectic Anthropology Server

... than in cultures coded as “4” (Dravidic). Even to such data, however, it is possible to apply some meaningful statistical methods. A special class of nominal data is constituted by dichotomous variables. We would like to stress that interval and nominal variables can be easily re-coded into dichotom ...
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Full Text in PDF - Gnedenko e

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Title of slide - WebHome < PP/Public < RHUL Physics

... equal or lesser compatibility with H relative to the data we got. This is not the probability that H is true! In frequentist statistics we don’t talk about P(H) (unless H represents a repeatable observation). In Bayesian statistics we do; use Bayes’ theorem to obtain ...
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T E C H N I C A L R E P O R T 10024 Prudence, temperance

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Deduction with Contradictions in Datalog

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Full Version

... mixed strategies, each of which may have a different “good response”, resulting in super-polynomially many strategies for the forecaster. Thus, the linear programming approach may yield a complex mixed strategy over super-polynomially many pure strategies, and so the universal cheating forecaster ma ...
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Comments about the Wilcoxon Rank Sum Test Scott S. Emerson

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Context-Sensitive Bayesian Description Logics

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Lec 11

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LNCS 8349 - 4-Round Resettably

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cowan_DESY_1 - Centre for Particle Physics

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Ars Conjectandi



Ars Conjectandi (Latin for The Art of Conjecturing) is a book on combinatorics and mathematical probability written by Jakob Bernoulli and published in 1713, eight years after his death, by his nephew, Niklaus Bernoulli. The seminal work consolidated, apart from many combinatorial topics, many central ideas in probability theory, such as the very first version of the law of large numbers: indeed, it is widely regarded as the founding work of that subject. It also addressed problems that today are classified in the twelvefold way, and added to the subjects; consequently, it has been dubbed an important historical landmark in not only probability but all combinatorics by a plethora of mathematical historians. The importance of this early work had a large impact on both contemporary and later mathematicians; for example, Abraham de Moivre.Bernoulli wrote the text between 1684 and 1689, including the work of mathematicians such as Christiaan Huygens, Gerolamo Cardano, Pierre de Fermat, and Blaise Pascal. He incorporated fundamental combinatorial topics such as his theory of permutations and combinations—the aforementioned problems from the twelvefold way—as well as those more distantly connected to the burgeoning subject: the derivation and properties of the eponymous Bernoulli numbers, for instance. Core topics from probability, such as expected value, were also a significant portion of this important work.
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