Emission probabilities in Hidden Markov Models represent the likelihood of observing a particular output given a specific hidden state, and these probabilities must sum to 1 for each hidden state. For example, if the hidden state is 'rainy', the probability of observing 'sad' is 0.9 while 'happy' is 0.1; if 'cloudy', 'sad' is 0.6 and 'happy' is 0.4; and if 'sunny', 'sad' is 0.2 and 'happy' is 0.8.
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Emission Probabilities in Hidden Markov Model追加:
Now, let us consider all the red arrows.
Okay?
So, these red arrows are called as emission probabilities. These red arrows show emission probabilities.
Okay?
So, emission means if the hidden state is known, what observation are we likely to see?
So, for example, if it is rainy, if rainy, then person sad.
So, the probability of that is 0.9.
As you can see over here.
If rainy, if rainy, then person happy.
What is the probability observe this arrow? The probability is 0.1.
Again, the total has to come out as one.
Okay?
Similarly, if you observe or cloudy, so cloudy to sad, probability is 0.6.
Cloudy to happy, the probability is 0.4.
Total is one.
And similarly, if uh it is sunny, the sunny to sad, the probability is 0.2.
And sunny to happy, the probability is 0.8.
As we all are enjoying the summers.
Right? So, >> Mhm.
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