Hidden Markov Model -HMMs
– analyse
– to predict time sence
Applications
– roboitcs
– medical
– finance
– speech
– language technology
HMMs follow bayes network
s1 -> s2 -> s3 -> ..Sn Markov chain
z1 z2 z3 z4
kalman filter, particle filter
localization problem
razor finder
speech recognition -> markov model
transition “I” to “a”
Hidden markov chain
P(R0)= 1
R(s0) = 0
p(R1) = 0.6
p(R2) = 0.44
P(R2) = 0.376
P(A1000)
P(A∞)lim t100 P(At)
stationary distribution
P(at) = P(at-1)
p(at|at-1)p(at-1) + P(at|t-1)P(bt-1)
