Hello guys, I am trying to compute the likelihood for a hidden Markov chain problem using the forward recursive process, described in the attached pdf,
LikelihoodComputation.pdf <http://r.789695.n4.nabble.com/file/n4722233/LikelihoodComputation.pdf> My goal is to find the parameters beta and mu that will maximize the above likelihood using R software and the following transition and emission matrices n=3 h=0.1 x=matrix(data=NA, nrow=n+1, ncol=n+1) # x is the transition matrix for(i in 0:n){ for(j in 0:n){ if(i==j){ x[i+1,j+1] = 1-beta*j*(n-j)*h-mu*j*h} else{ if(j==i+1){x[i+1,j+1] = beta*j*(n-j)*h} else{ if(j==i-1){ x[i+1,j+1] = j*mu*h} else{x[i+1,j+1] =0} }}}} x E=matrix(data=NA, nrow=n+1, ncol=n+1) # the emission matrix for(i in 0:n){ for(j in 0:n){ E[i+1,j+1]=dbinom(j, size=i, prob=0.59) }} E Can you please help me to program this using R , or just give me a hint how to start! Thanks in advance, Dandis -- View this message in context: http://r.789695.n4.nabble.com/Forward-Recursive-method-tp4722233.html Sent from the datatable-help mailing list archive at Nabble.com. _______________________________________________ datatable-help mailing list [email protected] https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help
