dlm.retro

the vector or matrix of the posterior mean (location parameter), dim = <code>p x T</code>, 
where <code>p</code> is the number of thetas (at any time <code>t</code>) and <code>T</code> is the number of time points

the posterior scale matrix with dim = <code>p x p x T</code> (unscaled by the observation variance)

the prior scale matrix with dim = <code>p x p x T</code> (unscaled by the observation variance)

vector of the updated hyperparameters for the precision <code>phi</code> with length <code>T</code>

Calculate the location and scale parameters for the time-varying coefficients 
given all the observations. West, M. &amp; Harrison, J., 1997. Bayesian Forecasting
and Dynamic Models. Springer New York.

Dynamic graphical models for multivariate time series data to estimate directed
dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et
al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.

Simon Schwab

Dynamic Graphical Models

dlm.retro function

Calculate the location and scale parameters for the time-varying coefficients 
given all the observations. West, M. &amp; Harrison, J., 1997. Bayesian Forecasting
and Dynamic Models. Springer New York. — dlm.retro

Calculate the location and scale parameters for the time-varying coefficients 
given all the observations. West, M. &amp; Harrison, J., 1997. Bayesian Forecasting
and Dynamic Models. Springer New York.

dlm.retro: Calculate the location and scale parameters for the time-varying coefficients given all the observations. West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.

Description

Usage

Arguments

Value