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Mogeng / IOHMM

Input Output Hidden Markov Model (IOHMM) in Python
BSD 3-Clause "New" or "Revised" License
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Emissions Probability Distribution #28

Open antonionieto opened 4 years ago

antonionieto commented 4 years ago

Hi!

I have adjusted an unsupervised IOHMM with 3 states and an emission covariate (see code below).

SHMM = UnSupervisedIOHMM(num_states=3, max_EM_iter=200, EM_tol=1e-4)

SHMM.set_models(model_emissions = [OLS(est_stderr=True)], model_transition=CrossEntropyMNL(solver='lbfgs'), model_initial=CrossEntropyMNL(solver='lbfgs'))

SHMM.set_inputs(covariates_initial = [], covariates_transition = [], covariates_emissions = [['var']])

SHMM.set_outputs([['out']])

SHMM.set_data([df])

SHMM.train()

I am trying to get the Emissions Probability Distribution in each state based on the emission covariate value. How can I model this probability? I am getting two coefficients and two estimated standard error coefficients of the emission model in each state.

How can I use these coefficients?

Appreciate your answers!

ucabqll commented 2 years ago

hello, were you able to relate the coefficients back to the distribution?