full_data

day_of_observations

day_dts

<p>Data simulated using the BDgraph package.  A change-point is imposed between days 5
and 6.  The change only occurs in variables 3 and 4.</p>

datasets

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

full_data function

<p>`full_data` is a matrix, the latter two are vectors.</p>

Format

Simulated Data with Imposed Change-points. — full_data

Simulated Data with Imposed Change-points.

full_data: Simulated Data with Imposed Change-points.

Description

Arguments

Format

Details

Examples