plot.bayesWatch

Print function for a bayesWatch object. Prints only the posterior change-point probabilities.

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

plot.bayesWatch function

<dl><dt>x</dt>
<dd>bayesWatch object. Fit from bayesWatch main method.</dd>
<dt>...</dt>
<dd>Additional plotting arguments.</dd></dl>

Arguments

Print function for a bayesWatch object.  Prints only the posterior change-point probabilities. — plot.bayesWatch

<dl>

<dt>x</dt>
<dd>bayesWatch object. Fit from bayesWatch main method.</dd>


<dt>...</dt>
<dd>Additional plotting arguments.</dd>

</dl>

plot.bayesWatch: Print function for a bayesWatch object. Prints only the posterior change-point probabilities.

Description

Arguments