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Create a random variable for HiddenMarkovModel.
tfp.edward2.HiddenMarkovModel( *args, **kwargs )
See HiddenMarkovModel for more details.
Original Docstring for Distribution
Initialize hidden Markov model.
Categorical-like instance. Determines probability of first hidden state in Markov chain. The number of categories must match the number of categories of
transition_distributionas well as both the rightmost batch dimension of
transition_distributionand the rightmost batch dimension of
Categorical-like instance. The rightmost batch dimension indexes the probability distribution of each hidden state conditioned on the previous hidden state.
tfp.distributions.Distribution-like instance. The rightmost batch dimension indexes the distribution of each observation conditioned on the corresponding hidden state.
num_steps: The number of steps taken in Markov chain. A python
Truedistribution parameters are checked for validity despite possibly degrading runtime performance. When
Falseinvalid inputs may silently render incorrect outputs. Default value:
True, statistics (e.g., mean, mode, variance) use the value "
NaN" to indicate the result is undefined. When
False, an exception is raised if one or more of the statistic's batch members are undefined. Default value:
strname prefixed to Ops created by this class. Default value: "HiddenMarkovModel".
num_stepsis not at least 1.
initial_distributiondoes not have scalar
transition_distributiondoes not have scalar
observation_distributionare fully defined but don't have matching rightmost dimension.