Global multivariate model learning from hierarchically correlated data

Simulating Correlated Multivariate Normal Data

Multivariate linear systems for learning from data

Learning from data means a process of information extraction from finite size samples in order to estimate an unknown dependency. A series of problems can be modeled in terms of a system that computes according to an unknown rule a response (output) for each input. The paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The...

Learning Bayesian Networks from Correlated Data

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between obs...

Learning timbre analogies from unlabelled data by multivariate tree regression

Applications such as concatenative synthesis (audio mosaicing) and query-by-example require the ability to search a database using a sound which is qualitatively different from the actual desired result – for example when using vocal queries to retrieve nonvocal sound. Standard query techniques such as nearest neighbours do not account for this difference between source and target; they perform...

Learning Treatment-Response Models from Multivariate Longitudinal Data

Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our proposed solution represents the treatment response curves using linear time-invari...