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28
Parameter Learning in Object Oriented Bayesian Networks
, 2001
"... This paper describes a method for parameter learning in ObjectOriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in objectoriented domains. We a ..."
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Cited by 13 (5 self)
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This paper describes a method for parameter learning in ObjectOriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in objectoriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are not strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to objectoriented domains
Predictive Specification of Prior Model Probabilities in Variable selection
 80 C.L. Mallows
, 1996
"... ..."
Transferring Prior Information Between Models Using Imaginary Data
, 2001
"... . Bayesian modeling is limited by our ability to formulate prior distributions that adequately represent our actual prior beliefs  a task that is especially difficult for realistic models with many interacting parameters. I show here how a prior distribution formulated for a simpler, more easily ..."
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Cited by 6 (0 self)
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. Bayesian modeling is limited by our ability to formulate prior distributions that adequately represent our actual prior beliefs  a task that is especially difficult for realistic models with many interacting parameters. I show here how a prior distribution formulated for a simpler, more easily understood model can be used to modify the prior distribution of a more complex model. This is done by generating imaginary data from the simpler "donor" model, which is conditioned on in the more complex "recipient" model, effectively transferring the donor model's wellspecified prior information to the recipient model. Such prior information transfers are also useful when comparing two complex models for the same data. Bayesian model comparison based on the Bayes factor is very sensitive to the prior distributions for each model's parameters, with the result that the wrong model may be favoured simply because the prior for the right model was not carefully formulated. This problem can be alleviated by modifying each model's prior to potentially incorporate prior information transferred from the other model. I discuss how these techniques can be implemented by simple Monte Carlo and by Markov chain Monte Carlo with annealed importance sampling. Demonstrations on models for twoway contingency tables and on graphical models for categorical data show that prior information transfer can indeed overcome deficiencies in prior specification for complex models.
Relaxing the Local Independence Assumption for Quantitative Learning in Acyclic Directed Graphical Models through Hierarchical Partition Models
 Proceedings of Artificial Intelligence and Statistics ’99
, 1999
"... The simplest method proposed by Spiegelhalter and Lauritzen (1990) to perform quantitative learning in ADG presents a potential weakness: the local independence assumption. We propose to alleviate this problem through the use of Hierarchical Partition Models. Our approach is compared with the previo ..."
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Cited by 6 (0 self)
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The simplest method proposed by Spiegelhalter and Lauritzen (1990) to perform quantitative learning in ADG presents a potential weakness: the local independence assumption. We propose to alleviate this problem through the use of Hierarchical Partition Models. Our approach is compared with the previous one from an interpretative and predictive point of view. 1 INTRODUCTION Spiegelhalter and Lauritzen (1990) (SL) proposed a Bayesian model for Acyclic Directed Graphical Models (ADG) (also known as Bayesian Networks) that has become somewhat standard in the burgeoning literature on learning discrete graphical models. The basic idea is to treat the conditional probabilities of the random variables at each vertex in the graph as unknowns and associate a prior distribution on each one (the conditioning in each case is on the random variables associated with the parent vertices in the graph). The simplest approach of SL introduces strong assumptions on the unknown conditional probabilities ...
Information Fusion, Causal Probabilistic Network And Probanet II: Inference Algorithms and Probanet System
 Proc. 1st Intl. Workshop on Image Analysis and Information Fusion
, 1997
"... As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the ..."
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Cited by 2 (2 self)
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As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematicsoriented literatures, with gaps lled in with regards to computability and completeness. In particular, possible optimizations on causal tree algorithm, graph triangulation and junction tree algorithm are discussed. Probanet has been designed and developed as a generic shell, or say, mother system for CPN construction and application. The design aspects and current status of Probanet are described. A few directions for research and system development are pointed out, including hierarchical structuring of network, structure decomposition and adaptive inference algorithms. This paper thus has a nature of integration including literature review, algorithm formalization and future perspective.
Bayesian Data Analysis for Data Mining
 In Handbook of Data Mining
, 2002
"... Introduction The Bayesian approach to data analysis computes conditional probability distribu tions of quantities of interest (such as future observables) given the observed data. Bayesian analyses usually begin with a .full probability model  a joint probability dis tribution for all the observ ..."
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Cited by 1 (0 self)
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Introduction The Bayesian approach to data analysis computes conditional probability distribu tions of quantities of interest (such as future observables) given the observed data. Bayesian analyses usually begin with a .full probability model  a joint probability dis tribution for all the observable and unobservable quantities under study  and then use Bayes' theorem (Bayes, 1763) to compute the requisite conditional probability distributions (called poster'Joy distributions). The theorem itself is innocuous enough. In its simplest form, if Q denotes a quantity of interest and D denotes data, the theorem states: P(ql D) P(;lq) X P(q)/P(). This theorem prescribes the basis for statistical learning in the probabilistic frame work. With p(Q) regarded as a probabilistic statement of prior knowledge about Q before obtaining the data D, p(QI D) becomes a revised probabilistic statement of our knowledge about Q in the light of the data (Bernardo and Smith, 1994, p.2). The marginal lik
Strategies for Model Mixing in Generalized Linear Models
"... this article, we present several new approaches that extend the results of Clyde, DeSimone, and Parmigiani (1996) and Clyde, Parmigiani, and Vidakovic (1995) to generalized linear models, leading to improved convergence of MCMC methods. 2 Models ..."
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this article, we present several new approaches that extend the results of Clyde, DeSimone, and Parmigiani (1996) and Clyde, Parmigiani, and Vidakovic (1995) to generalized linear models, leading to improved convergence of MCMC methods. 2 Models
DOI: 10.1080/10635150490522304 Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests
"... Abstract.—Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects o ..."
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Abstract.—Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (modelaveraged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AICbased model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus (genus Carabus) ground beetles described by Sota and Vogler (2001). [AIC; Bayes factors; BIC; likelihood ratio tests; model averaging; model uncertainty; model selection; multimodel inference.] It is clear that models of nucleotide substitution (henceforth models of evolution) play a significant role
Aqsaqal Enterprises
"... Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative is usually viewed as building classifiers by hand, using an expert’s understanding of what features of the text are related to the cla ..."
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Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative is usually viewed as building classifiers by hand, using an expert’s understanding of what features of the text are related to the class of interest. This is expensive, requires a degree of computational and linguistic sophistication, and makes it difficult to use combinations of weak predictors. We propose instead combining domain knowledge with training examples in a Bayesian framework. Domain knowledge is used to specify a prior distribution for parameters of a logistic regression model, and labeled training data is used to produce and find the mode of the posterior distribution. We show on three text categorization data sets that this approach can rescue what would otherwise be disastrously bad training situations, producing much more effective classifiers.