Results 1 
4 of
4
Univariate Polynomial Inference by Monte Carlo Message Length Approximation
 in Int. Conf. Machine Learning
, 2002
"... We apply the Message from Monte Carlo (MMC) algorithm to inference of univariate polynomials. MMC is an algorithm for point estimation from a Bayesian posterior sample. ..."
Abstract

Cited by 9 (5 self)
 Add to MetaCart
We apply the Message from Monte Carlo (MMC) algorithm to inference of univariate polynomials. MMC is an algorithm for point estimation from a Bayesian posterior sample.
Minimum Message Length Grouping of Ordered Data
 Proceedings of the Eleventh International Conference on Algorithmic Learning Theory (ALT2000), LNAI
, 2000
"... Explicit segmentation is the partitioning of data into homogeneous regions by specifying cutpoints. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Progr ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
Explicit segmentation is the partitioning of data into homogeneous regions by specifying cutpoints. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Programming Algorithm (DPA). Oliver, Baxter and colleagues (1996,1997,1998) have applied the informationtheoretic Minimum Message Length (MML) principle to explicit segmentation. Given a series of multivariate data, approximate it by a piecewise constant function. How many cutpoints are there? What are the means and variances of each segment? Where should the cut points be placed? The simplest model is a single segment. The most complex model has one segment per data point. The best model is generally somewhere between these extremes. Only by considering model complexity can a reasonable inference be made.
ChangePoint Estimation Using New Minimum Message Length Approximations
 Proc. PRICAI
, 2002
"... This paper investigates the coding of changepoints in the informationtheoretic Minimum Message Length (MML) framework. Changepoint coding regions affect model selection and parameter estimation in problems such as time series segmentation and decision trees. The Minimum Message Length (MML) and Mi ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This paper investigates the coding of changepoints in the informationtheoretic Minimum Message Length (MML) framework. Changepoint coding regions affect model selection and parameter estimation in problems such as time series segmentation and decision trees. The Minimum Message Length (MML) and Minimum Description Length (MDL78) approaches to changepoint problems have been shown to perform well by several authors. In this paper we compare some published MML and MDL78 methods and introduce some new MML approximations called `MMLDc' and `MMLDF'. These new approximations are empirically compared with Strict MML (SMML), Fairly Strict MML (FSMML), MML68, the Minimum Expected KullbackLeibler Distance (MEKLD) loss function and MDL78 on a tractable binomial changepoint problem.
Bayesian Inference for Reliable Biomedical Signal Processing
, 2000
"... This thesis investigates whether Bayesian inference can improve the reliability of biomedical diagnosis. In particular we discuss time series classi cation as is for example needed for an analysis of allnight sleep EEG recordings. Such an attempt needs 4 steps that are further analyzed. ..."
Abstract
 Add to MetaCart
This thesis investigates whether Bayesian inference can improve the reliability of biomedical diagnosis. In particular we discuss time series classi cation as is for example needed for an analysis of allnight sleep EEG recordings. Such an attempt needs 4 steps that are further analyzed.