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HMMLearn Predict Next Observed Event
2019 Community Moderator ElectionHow can I predict the post popularity of reddit.com with hidden markov model(HMM)?Markov chain modelling?How to interpret the AUC score in this case?Python - Get FP/TP from Confusion Matrix using a ListMarkov Chains for sequential dataSimple Markov Chains Memoryless Property QuestionHow to set the parameters of a Hidden Markov Model that'll be used to correct the mistakes by a previous classifier?Predict next month's loan balance from historical dataUnequal length observation sequences when training Hidden Markov modelWhy are observation probabilities modelled as Gaussian distributions in HMM?
$begingroup$
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
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bumped to the homepage by Community♦ 14 hours ago
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
$endgroup$
bumped to the homepage by Community♦ 14 hours ago
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♦ 14 hours ago
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♦ 14 hours ago
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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$begingroup$
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
5431211
$endgroup$
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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
5431211
$endgroup$
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
5431211
$endgroup$
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
5431211
$endgroup$
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
5431211
edited Aug 10 '18 at 21:26
answered Aug 10 '18 at 21:09
pythinkerpythinker
5431211
answered Aug 10 '18 at 21:09
pythinkerpythinker
5431211
answered Aug 10 '18 at 21:09
pythinkerpythinker
5431211
5431211
add a comment |
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