Comments (1)
Hi,
Here is an example with the Weather guessing game from Wikipedia.
Does that makes things clearer?
# K: number of states
# a[i] = probability of starting in i
# vector of length K
start_probability = [0.6, 0.4]
# A[i,j] = probability of going from i to j
# matrix of size KxK
transition_probability = [
0.7 0.3; # i=1 (Rainy)
0.4 0.6 # i=2 (Sunny)
]
# B[i] = probability distribution of the observations in state i
# vector of size K
# We can model an emission matrix with the discrete `Categorical` distribution:
emission_probability = [
# [walk, shop, clean]
Categorical([0.1, 0.4, 0.5]), # i=1 (Rainy)
Categorical([0.6, 0.3, 0.1]), # i=2 (Sunny)
]
hmm = HMM(start_probability, transition_probability, emission_probability)
from hmmbase.jl.
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from hmmbase.jl.