Open GoogleCodeExporter opened 8 years ago
Indeed, given the input HMM model with N+1 states S_0,...N, and an input
observation sequence of M+1 symbols O_0,...,O_m:
The probability for the state at time t, q[t], to be equal to S_i can be
ForwardProbability(q[t]==S_i|O_0,...,O_t) = alpha[t][i]/(sum_{j=0..N}
alpha[t][j])
BackwardProbability(q[t]=S_i|O_{t+1},...,O_m) = beta[t][i]/(sum_{j=0..N}
beta[t][j])
PosteriorProbability(q[t]=S_i|O_0,...,O_m) =
alpha[t][i]*beta[t][i]/(sum_{j=0..N} alpha[t][j]*beta[t][j])
This can be easily implemented in the HMM class.
Original comment by changzho...@gmail.com
on 19 Jul 2011 at 3:51
Original issue reported on code.google.com by
changzho...@gmail.com
on 18 Jul 2011 at 6:06