CABS

Conditional Adaptive Bayesian Spectrum Analysis (CABS)
"Adaptive Bayesian Spectral Analysis of Nonstationary Biomedical Time Series"
by Bruce, Hall, Buysse, and Krafty (2017)

Author: Scott A. Bruce

Description:

Instructions for implementing CABS estimation from "Adaptive Bayesian
Spectral Analysis of Nonstationary Biomedical Time Series"
by Bruce, Hall, Buysse, and Krafty (2017)

Dependencies:

Code was developed using MATLAB 2016b (version 1.0.0) and has since been updated to be compatible with MATLAB 2018a (version 2.0.0), so code may not function properly
on other versions of MATLAB. If choose parallel computing option (parcomp=1), the Parallel
Computing Toolbox is required.

Code has been tested and developed for data of the following dimensions:
<= 5000 time points, <= 100 subjects, <= 50000 iterations.
Larger datasets may require more RAM for processing. Reduce batch sizes
in MCMC settings to reduce required RAM by outputting more frequently
to dataset stored in ROM.

Quick start guide:

Follow the steps below to simulate data, run the CABS estimation procedure,
and create data visualizations to assess the convergence and fit of the
method to simulated slowly varying and piecewise AR processes detailed in
the paper.

1. Download all files and folders into a folder of your
   choosing. In what follows, we will assume the folder is saved with the
   following path 'C:\CABSdemo'.

2. Open MATLAB (version 2016b or newer is recommended) and change your
   current working directory to 'C:\CABSdemo\programs'. This
   directory contains all of the MATLAB functions needed to run the MCMC sampler.

3. Open the file 'C:\CABSdemo\demo.m'. This script contains
   all necessary code to reproduce the data and MCMC sampler results for the
   piecewise and slowly varying AR processes.

4. Follow the instructions in the comments of the demo file to simulate
   data from the piecewise and slowly varying AR processes described in the
   paper and apply the CABS procedure to obtain spectral estimates and summary
   plots of the MCMC sampler fit. There is an additional section which
   demonstrates how to obtain the convergence diagnostic measures and plots
   used to assess convergence.

Using CABS on other data:

Three inputs are necessary to run the MCMC sampler for the CABS estimation
procedure.

1. 'x' is a matrix whereby each column contains a realization of the time
   series for a given subject. The rows are indexed by time
   (t=1,...,T) and the columns are indexed by subject (l=1,...,L). For
   example, {x}_11 contains the first time series instance for the first
   subject.

2. 'u' is a vector containing the corresponding covariate values for each
   subject. The columns are indexed by subject, which is the same as for
   the time series matrix 'x'. For example, {u}_1 is the covariate value
   for the first subject.

3. 'opt' is a structure containing the options required for the MCMC
   sampler to run. See 'setMCMCOptions.m' for descriptions of all of the
   options as well as the default values. Once you have determined the
   appropriate settings for your data, use this function to create the
   structure needed as in the demo file.

Once you have created these three inputs based on your data, you can pass
them into the 'CABS' function just as in the demo file for estimation.