get_point_estimate

Given a bayesWatch object and a probability cutoff, finds change-points.

Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.

Alexander C. Murph

bayesWatch

Bayesian Change-Point Detection for Process Monitoring with
Fault Detection

Reza Mohammadi

Alex Lenkoski

Andrew Johnson

get_point_estimate function

<dl><dt>regime_fit_object</dt>
<dd>bayesWatch object. Fit with the bayesWatch method.</dd>
<dt>prob_cutoff</dt>
<dd>float in (0,1). Posterior probabilities above this cutoff will be considered changepoints.</dd></dl>

Arguments

Create an estimate on posterior distribution of change-points. — get_point_estimate

<dl>

<dt>regime_fit_object</dt>
<dd>bayesWatch object. Fit with the bayesWatch method.</dd>


<dt>prob_cutoff</dt>
<dd>float in (0,1). Posterior probabilities above this cutoff will be considered changepoints.</dd>

</dl>

get_point_estimate: Create an estimate on posterior distribution of change-points.

Description

Usage

Value

Arguments