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Results 1 - 10
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94
Factor models for multivariate count data
- Journal of Multivariate Analysis
, 2003
"... Abstract We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety of distribu ..."
Abstract
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Cited by 2 (0 self)
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Abstract We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety
Gradient-based boosting for Statistical Relational Learning: The Relational Dependency Network Case
, 2011
"... Abstract. Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, co ..."
Abstract
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Cited by 39 (17 self)
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function-approximation problems using gradient-based boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient
Boosting relational dependency networks
- In Proc. of the Int. Conf. on Inductive Logic Programming (ILP
, 2010
"... Abstract. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains where the joint probability distribution over the variables is approximated as a product of conditional distributions. The current learning algorithms for RDNs use pseudolikelih ..."
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Cited by 5 (3 self)
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Abstract. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains where the joint probability distribution over the variables is approximated as a product of conditional distributions. The current learning algorithms for RDNs use
Poisson-Networks: A Model for Structured Point Processes
- IN AI AND STATISTICS 10
, 2005
"... Modelling structured multivariate point process data has wide ranging applications like understanding neural activity, developing faster file access systems and learning dependencies among servers in large networks. In this paper, we develop the Poisson network model for representing multivari ..."
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Cited by 16 (0 self)
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Modelling structured multivariate point process data has wide ranging applications like understanding neural activity, developing faster file access systems and learning dependencies among servers in large networks. In this paper, we develop the Poisson network model for representing
Models of Insurance Claim Counts with Time Dependence Based on Generalization of Poisson and Negative Binomial Distributions
"... Longitudinal data (or panel data) consist of repeated ob-servations of individual units that are observed over time. Each individual insured is assumed to be independent but correlation between contracts of the same individual is permitted. This paper presents an exhaustive overview of models for pa ..."
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for panel data that consist of generalizations of count distributions where the dependence between con-tracts of the same insureds can be modeled with Bayesian and frequentist models, based on generalization of Poisson and negative binomial distributions. This paper introduces some of those models
Modeling short-term noise dependence of spike counts in macaque prefrontal cortex
- Advances in Neural Information Processing Systems 21
, 2009
"... Correlations between spike counts are often used to analyze neural coding. The noise is typically assumed to be Gaussian. Yet, this assumption is often inappro-priate, especially for low spike counts. In this study, we present copulas as an alternative approach. With copulas it is possible to use ar ..."
Abstract
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Cited by 2 (1 self)
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arbitrary marginal distri-butions such as Poisson or negative binomial that are better suited for modeling noise distributions of spike counts. Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order in-teractions. We develop a framework
Learning microbial interaction networks from metagenomic count data
"... Abstract. Many microbes associate with higher eukaryotes and impact their vitality. In order to engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenence, which in large part, demands an understanding of the direct interactions between community membe ..."
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members. Toward this end, we've developed a Poissonmultivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer, and captures direct taxon
Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation
"... Simultaneous spike-counts of neural populations are typically modeled by a Gaussian distribution. On short time scales, however, this distribution is too restrictive to describe and analyze multivariate distributions of discrete spike-counts. We present an alternative that is based on copulas and ca ..."
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Cited by 4 (0 self)
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and can account for arbitrary marginal distributions, including Poisson and negative binomial distributions as well as second and higher-order interactions. We describe maximum likelihood-based procedures for fitting copula-based models to spike-count data, and we derive a so-called flashlight
likelihood and the role of models in molecular phylogenetics.
- Mol. Biol. Evol.
, 2000
"... Methods such as maximum parsimony (MP) are frequently criticized as being statistically unsound and not being based on any ''model.'' On the other hand, advocates of MP claim that maximum likelihood (ML) has some fundamental problems. Here, we explore the connection between the ..."
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Cited by 70 (11 self)
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H, given data D and a specific model, is proportional to P(D ͦ H), the conditional probability of observing D given that H is correct In the context of phylogeny reconstruction from sequences, D typically counts the number of ''site patterns'' that occur in a collection
Results 1 - 10
of
94
