Emrys::HiddenMarkovFilterUpdate< Scalar > Class Template Reference

What a hidden Markov filter's update did: the measurement's log-evidence under the prior, and whether it was accepted. More...

#include <EmrysFilter/Filters/HiddenMarkovFilter.h>

Public Member Functions
Scalar	logLikelihood () const

Public Attributes
bool	accepted = false
	whether some value with a nonzero probability could explain the measurement

Detailed Description

template<typename Scalar = double>
class Emrys::HiddenMarkovFilterUpdate< Scalar >

What a hidden Markov filter's update did: the measurement's log-evidence under the prior, and whether it was accepted.

When accepted is false, no value with a nonzero probability could explain the measurement, its log-evidence is \( -\infty \), and the state was left as it was (decisions.md [update-outcomes]).

Member Function Documentation

logLikelihood()

template<typename Scalar = double>

Scalar Emrys::HiddenMarkovFilterUpdate< Scalar >::logLikelihood ( ) const

inlinenodiscard

Returns

\( \log p(z_k \mid z_{1:k-1}) = \log \sum_v p_v \, p(z_k \mid v) \)

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The documentation for this class was generated from the following file:

• EmrysFilter/Filters/HiddenMarkovFilter.h