HMMLearn Predict Next Observed Event
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From my understanding you can use the transition matrix to predict the probability of going from the last predicted hidden state(state t), to the t+1 hidden state. My confusion is how in code format do I go from the hidden state predicted at time t+1 to the predicted observed state at time t+1.
python markov-hidden-model markov
asked Aug 10 '18 at 19:17
FemiFemi
161
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$begingroup$
From my understanding you can use the transition matrix to predict the probability of going from the last predicted hidden state(state t), to the t+1 hidden state. My confusion is how in code format do I go from the hidden state predicted at time t+1 to the predicted observed state at time t+1.
python markov-hidden-model markov
asked Aug 10 '18 at 19:17
FemiFemi
161
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
From my understanding you can use the transition matrix to predict the probability of going from the last predicted hidden state(state t), to the t+1 hidden state. My confusion is how in code format do I go from the hidden state predicted at time t+1 to the predicted observed state at time t+1.
python markov-hidden-model markov
asked Aug 10 '18 at 19:17
FemiFemi
161
$endgroup$
From my understanding you can use the transition matrix to predict the probability of going from the last predicted hidden state(state t), to the t+1 hidden state. My confusion is how in code format do I go from the hidden state predicted at time t+1 to the predicted observed state at time t+1.
python markov-hidden-model markov
python markov-hidden-model markov
asked Aug 10 '18 at 19:17
FemiFemi
161
asked Aug 10 '18 at 19:17
FemiFemi
161
asked Aug 10 '18 at 19:17
FemiFemi
161
asked Aug 10 '18 at 19:17
FemiFemi
161
asked Aug 10 '18 at 19:17
FemiFemi
161
161
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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For each state of continuous HMM, an emission probability distribution is defined. It can be a Gaussian distribution or a Gaussian Mixture Model (GMM). After computing the state variable, you can get the emission probability distribution of observed variables using corresponding state distribution. Note that there is no deterministic value for observed variables and just a probability distribution conditioned on the predicted state variable. In hmmlearn, you can get the means, covars and priors of the GMM using means_, covars_ and weights_ attributes of the model, respectively.
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
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1 Answer
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1 Answer
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$begingroup$
For each state of continuous HMM, an emission probability distribution is defined. It can be a Gaussian distribution or a Gaussian Mixture Model (GMM). After computing the state variable, you can get the emission probability distribution of observed variables using corresponding state distribution. Note that there is no deterministic value for observed variables and just a probability distribution conditioned on the predicted state variable. In hmmlearn, you can get the means, covars and priors of the GMM using means_, covars_ and weights_ attributes of the model, respectively.
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
$endgroup$
add a comment |
$begingroup$
For each state of continuous HMM, an emission probability distribution is defined. It can be a Gaussian distribution or a Gaussian Mixture Model (GMM). After computing the state variable, you can get the emission probability distribution of observed variables using corresponding state distribution. Note that there is no deterministic value for observed variables and just a probability distribution conditioned on the predicted state variable. In hmmlearn, you can get the means, covars and priors of the GMM using means_, covars_ and weights_ attributes of the model, respectively.
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
$endgroup$
add a comment |
$begingroup$
For each state of continuous HMM, an emission probability distribution is defined. It can be a Gaussian distribution or a Gaussian Mixture Model (GMM). After computing the state variable, you can get the emission probability distribution of observed variables using corresponding state distribution. Note that there is no deterministic value for observed variables and just a probability distribution conditioned on the predicted state variable. In hmmlearn, you can get the means, covars and priors of the GMM using means_, covars_ and weights_ attributes of the model, respectively.
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
$endgroup$
For each state of continuous HMM, an emission probability distribution is defined. It can be a Gaussian distribution or a Gaussian Mixture Model (GMM). After computing the state variable, you can get the emission probability distribution of observed variables using corresponding state distribution. Note that there is no deterministic value for observed variables and just a probability distribution conditioned on the predicted state variable. In hmmlearn, you can get the means, covars and priors of the GMM using means_, covars_ and weights_ attributes of the model, respectively.
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
edited Aug 10 '18 at 21:26
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
answered Aug 10 '18 at 21:09
pythinkerpythinker
5831211
5831211
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