python,string,count,probability,markov-chains

Two things: This isn't related to Markov Chains. At All. Python actually has some really nice builtins that will make this more or less trivial. I won't spoon-feed an answer, but I don't want to leave you high and dry on this one. The gist is that depending on your...

r,for-loop,nested-loops,markov-chains,markov

Your y <- y+1 is in one too many curly brackets. It is not being called most of the time. I think this is what you want: x1 <- runif(100, 1.0, 100) markov <- matrix(c(.5,.3,.2,.4,.5,.1,.2,.4,.4),nrow=3) m1 <- markov[1:3] m2 <- markov[4:6] m3 <- markov[7:9] y <- 2 x <- 1...

machine-learning,statistics,probability,montecarlo,markov-chains

I think I found exactly what I want, which appears in last year's NIPS conference: "Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions" Ari Pakman et al. http://www.stat.columbia.edu/~liam/research/pubs/pakman-exact-binary-hmc.pdf...

matlab,random,weight,markov-chains

Use this transition matrix of your Markov chain: M = 1/9*[2, 3, 2, 2; ... 3, 2, 2, 2; ... 2, 2, 2, 3; ... 3, 2, 2, 2]; with the following algorithm: function realizations = realizeMarkovChain(M, start, numSteps) %// Generates realization of Markov chain given by transition matrix M....

r,probability,markov-chains,mcmc,mixture-model

There are of course other ways to do this, but the distr package makes it pretty darned simple. (See also this answer for another example and some more details about distr and friends). library(distr) ## Construct the distribution object. myMix <- UnivarMixingDistribution(Norm(mean=2, sd=8), Cauchy(location=25, scale=2), Norm(mean=10, sd=6), mixCoeff=c(0.4, 0.2, 0.4))...

python,list,table,dictionary,markov-chains

Assuming your file has no other punctuation (easy enough to strip out): import itertools def pairwise(s): a,b = itertools.tee(s) next(b) return zip(a,b) counts = [[0 for _ in range(52)] for _ in range(52)] # nothing has occurred yet with open('path/to/input') as infile: for a,b in pairwise(char for line in infile...

matlab,probability,markov-chains

You code A(S,S)*A(S,C) + A(S,R)*A(R,C) + A(S,C)*A(C,C) (i.e. sum over all possible intermediate states, or Chapman-Kolmogorov equation) is just matrix multiplication: A(S,:)*A(:,C) In general, A2 = A^2 gives the probabilty of all such double transitions, and An = A^n is the probability of n-order transitions (see for example here). So...

Are you running msm 1.5? In the changelog (http://cran.r-project.org/web/packages/msm/ChangeLog) it is mentioned that a bug has been fixed that led to infinite loops on windows. If your time-series has several short jumps you might get a log-likelihood underflow. You can study this using fixedpars = TRUE in the msm call...

math,probability,markov-chains,random-walk

Your state has to contain the last position, so that you have transitions (-1,-1) --> (-1,-1) (+1,+1) --> (+1,+1) with 70% probability and (-1,+1) --> (+1,-1) (+1,-1) --> (-1,+1) with 30% probability each....

python,numpy,linear-algebra,eigenvector,markov-chains

Is the transition matrix symmetric? If not, consider checking for T.T (the transpose), because you need to make sure you're looking at the right state transitions: you need the left eigenvector of your stochastic matrix, but almost all out-of-the-box scientific packages (numpy included) default to computing the right eigenvectors (this...