Publication

Asymptotically Optimal Matching of Multiple Sequences to Source Distributions and Training Sequences

Jayakrishnan Unnikrishnan
2015
Journal Articles

Abstract

Consider a finite set of sources, each producing independent identically distributed observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the constraint that the observed sequences are produced by distinct sources. In general, the number of sequences N may be different from the number of sources M, and only some K

Official source

https://infoscience.epfl.ch/entities/publication/7f029003-aad4-477a-8104-bb1acb58d97a

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Jayakrishnan Unnikrishnan
2015
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/7f029003-aad4-477a-8104-bb1acb58d97a

About this result

Related concepts (32)

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Probability theory

Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space.

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

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Asymptotically Optimal Matching of Multiple Sequences to Source Distributions and Training Sequences

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