Lecture Notes in Mathematics - Probability and Information Theory
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Published By Springer Berlin Heidelberg

9783540046080, 9783540360988

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Boolean algebraic methods in Markov chains

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079116 ◽

1969 ◽

pp. 55-63

Author(s):

M. Barahamihir ◽

D. Behara

Keyword(s):

Markov Chains ◽

Algebraic Methods

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Some mathematical problems in statistical mechanics

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079121 ◽

1969 ◽

pp. 106-124

Author(s):

M. Kac

Keyword(s):

Statistical Mechanics ◽

Mathematical Problems

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Asymptotic behaviour of the average probability of error for low rates of information transmission

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079122 ◽

1969 ◽

pp. 125-125

Author(s):

N. S. Kambo ◽

S. Kotz

Keyword(s):

Asymptotic Behaviour ◽

Information Transmission ◽

Probability Of Error ◽

Average Probability

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Convergence in probability and allied results

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079126 ◽

1969 ◽

pp. 186-186

Author(s):

A. R. Padmanabhan

Keyword(s):

Convergence In Probability

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Series expansions for random processes

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079118 ◽

1969 ◽

pp. 77-95 ◽

Cited By ~ 4

Author(s):

L. L. Campbell

Keyword(s):

Random Processes ◽

Series Expansions

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Maximum probability estimators with a general loss function

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079130 ◽

1969 ◽

pp. 232-256 ◽

Cited By ~ 7

Author(s):

L. Weiss ◽

J. Wolfowitz

Keyword(s):

Loss Function ◽

Maximum Probability ◽

General Loss Function

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The central limit theorem and ε-entropy

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079129 ◽

1969 ◽

pp. 224-231 ◽

Cited By ~ 28

Author(s):

Volker Strassen ◽

R.M. Dudley

Keyword(s):

Central Limit Theorem ◽

Limit Theorem ◽

Central Limit ◽

The Central Limit Theorem

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A necessary and sufficient condition for the validity of the local ergodic theorem

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079124 ◽

1969 ◽

pp. 170-177 ◽

Cited By ~ 3

Author(s):

Ulrich Krengel

Keyword(s):

Ergodic Theorem ◽

Sufficient Condition ◽

Necessary And Sufficient Condition ◽

Local Ergodic Theorem ◽

Necessary And Sufficient

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Glivenko-Cantelli type theorems for distance functions based on the modified empirical distribution function of M. Kac and for the empirical process with random sample size in general

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079119 ◽

1969 ◽

pp. 96-98

Author(s):

Miklós Csörgő

Keyword(s):

Distribution Function ◽

Sample Size ◽

Random Sample ◽

Empirical Process ◽

Empirical Distribution Function ◽

Empirical Distribution ◽

Distance Functions ◽

Random Sample Size

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Random processes defined through the interaction of an infinite particle system

Lecture Notes in Mathematics - Probability and Information Theory ◽

10.1007/bfb0079128 ◽

1969 ◽

pp. 201-223 ◽

Cited By ~ 13

Author(s):

Frank Spitzer

Keyword(s):

Particle System ◽

Random Processes ◽

Infinite Particle System ◽

Infinite Particle

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Lecture Notes in Mathematics - Probability and Information TheoryLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Boolean algebraic methods in Markov chains

Some mathematical problems in statistical mechanics

Asymptotic behaviour of the average probability of error for low rates of information transmission

Convergence in probability and allied results

Series expansions for random processes

Maximum probability estimators with a general loss function

The central limit theorem and ε-entropy

A necessary and sufficient condition for the validity of the local ergodic theorem

Glivenko-Cantelli type theorems for distance functions based on the modified empirical distribution function of M. Kac and for the empirical process with random sample size in general

Random processes defined through the interaction of an infinite particle system

Lecture Notes in Mathematics - Probability and Information Theory
Latest Publications