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ICMLA
2007

Maximum Likelihood Quantization of Genomic Features Using Dynamic Programming

14 years 26 days ago
Maximum Likelihood Quantization of Genomic Features Using Dynamic Programming
Dynamic programming is introduced to quantize a continuous random variable into a discrete random variable. Quantization is often useful before statistical analysis or reconstruction of large network models among multiple random variables. The quantization, through dynamic programming, finds the optimal discrete representation of the original probability density function of a random variable by maximizing the likelihood for the observed data. This algorithm is highly applicable to study genomic features such as the recombination rate across the chromosomes and the statistical properties of non-coding elements such as
Mingzhou (Joe) Song, Robert M. Haralick, Sté
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ICMLA
Authors Mingzhou (Joe) Song, Robert M. Haralick, Stéphane Boissinot
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