Gibbs sampling (also called alternating conditional sampling) is a Markov Chain Monte Carlo algorithm for high-dimensional data.
GibbsSampler is a motif finding algorithm that finds one common motif and returns a list of bestMotifs containing the closest motif match from each string in dna.
Given: Integers k, t, and N, followed by a collection of strings Dna.
Return: The strings BestMotifs resulting from running GibbsSampler(Dna, k, t, N) with 20 random starts.
We have to code Gibbs Sampler for the purpose of motif discovery. Here,
Dna -- A collection of DNA strings that are of the same length.
"t" -- Is an integer indicating how many times to read the genetic algorithm.
"k" -- An integer indicating the motif length being searched for.
"N" -- The number of iterations before returning the best motif.