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157,494
Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
"... In this chapter, we argue that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full ..."
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In this chapter, we argue that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full
Visualisation of tree-structured data through generative probabilistic modelling, in this volume
"... We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A natural measure of likelihood arises as a cost function that guides the model fitting. We compare our approach with an exist ..."
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Cited by 5 (1 self)
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We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A natural measure of likelihood arises as a cost function that guides the model fitting. We compare our approach
Generative probabilistic models extend the scope of inferential structure determination
- Journal of Magnetic Resonance 213: 182–186. PLOS Computational Biology | www.ploscompbiol.org 9 February 2014 | Volume 10 | Issue 2 | e1003406
, 2011
"... a b s t r a c t Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, ..."
Abstract
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Cited by 2 (1 self)
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in practice. Here, we demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models
Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling
"... Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models- a generalization of density-based visualization methods previously developed for static data sets. ..."
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Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models- a generalization of density-based visualization methods previously developed for static data sets
Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling
"... Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models- a generalization of density-based visualization methods previously developed for static data sets. ..."
Abstract
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Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models- a generalization of density-based visualization methods previously developed for static data sets
Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling
"... Recently, generative probabilistic modeling princi-ples were extended to visualization of structured data types, such as sequences. The models are for-mulated as constrained mixtures of sequence mod-els- a generalization of density-based visualization methods previously developed for static data set ..."
Abstract
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Recently, generative probabilistic modeling princi-ples were extended to visualization of structured data types, such as sequences. The models are for-mulated as constrained mixtures of sequence mod-els- a generalization of density-based visualization methods previously developed for static data
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
Abstract
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Cited by 1225 (10 self)
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model is able to deal with domain-specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments
Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories
, 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
Abstract
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Cited by 784 (16 self)
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proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model
Learning probabilistic relational models
- In IJCAI
, 1999
"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 613 (30 self)
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of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related
Results 1 - 10
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157,494
