Oct. 13 2008 10:45AM

Oct. 13 2008 1:30PM

In an AR(p) model of data evolution in a discrete time with a Gaussian noise uncorrelated between the data channels, we are trying to predict the distribution of data up to the horizon t + h, knowing the data at time t. Because of the use of Bayesian paradigm, there is a parameter uncertainty present in the model specifi ed by a known prior distribution.
Since the analytical solution of such a prediction is not tractable due to the high dimensionality of the problem (in this case the dimension is in nite), we are forced to search for approximative solutions. We propose one such solution using Monte Carlo sampling from the prior distribution and later reconstruction of the fi nal distribution.

Pravidelný seminář Rozhodování a řízení za neurčitosti. (Contact person: Miroslav Kárný)