Bayesian function learning using MCMC methods
Magni, P.
Bellazzi, R.
De Nicolao, G.
Dipartimento di Inf. e Sistemistica, Pavia Univ. ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Dec 1998
Volume: 20,
Issue: 12
On page(s): 1319-1331
ISSN: 0162-8828
References Cited: 36
CODEN: ITPIDJ
INSPEC Accession Number: 6131781
Digital Object Identifier: 10.1109/34.735805
Current Version Published: 2002-08-06
Abstract
The paper deals with the problem of reconstructing a continuous 1D
function from discrete noisy samples. The measurements may also be
indirect in the sense that the samples may be the output of a linear
operator applied to the function. Bayesian estimation provides a unified
treatment of this class of problems. We show that a rigorous Bayesian
solution can be efficiently implemented by resorting to a Markov chain
Monte Carlo (MCMC) simulation scheme. In particular, we discuss how the
structure of the problem can be exploited in order to improve the
computational and convergence performances. The effectiveness of the
proposed scheme is demonstrated on two classical benchmark problems as
well as on the analysis of IVGTT (IntraVenous glucose tolerance test)
data, a complex identification-deconvolution problem concerning the
estimation of the insulin secretion rate following the administration of
an intravenous glucose injection
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
