Results 11  20
of
640
Symbolic Variable Elimination for Discrete and Continuous Graphical Models
"... Probabilistic reasoning in the realworld often requires inference in continuous variable graphical models, yet there are few methods for exact, closedform inference when joint distributions are nonGaussian. To address this inferential deficit, we introduce SVE – a symbolic extension of the wellk ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
known variable elimination algorithm to perform exact inference in an expressive class of mixed discrete and continuous variable graphical models whose conditional probability functions can be wellapproximated as oblique piecewise polynomials with bounded support. Using this representation, we show that we can
Conditional Planning in the Discrete Belief Space
, 2005
"... We address the nonprobabilistic conditional planning problem with partial observability. Although backward search (dynamic programming) is the leading algorithm for probabilistic forms of conditional planning and for conditional planning with full observability, recent works on the nonprobabili ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
on the nonprobabilistic partially observable problem have concentrated on forward search. To show the high competitiveness of the backward approach and the benefits of dynamic programming type backups in the nonprobabilistic problem we propose a new framework for nonprobabilistic conditional planning with partial
and Tabulation of data Diagrammatic and Graphical representation of data. UNIT II
"... dispersion skewness and kurtosis Lorenz curve. UNIT III Elementary probability space Sample space discrete probability, independent events Mathematical and Statistical probabilityAxiomatic approach to probability Addition and multiplication theorems conditional probability – Bayes ’ theorem ..."
Abstract
 Add to MetaCart
dispersion skewness and kurtosis Lorenz curve. UNIT III Elementary probability space Sample space discrete probability, independent events Mathematical and Statistical probabilityAxiomatic approach to probability Addition and multiplication theorems conditional probability – Bayes ’ theorem
K.: Introduction to Graphical Modelling
 Handbook of Statistical Systems Biology
, 2011
"... The aim of this chapter is twofold. In the first part (Sections 12.1, 12.2 and 12.3) we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develo ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
Variables Graphical models are a class of statistical models which combine the rigour of a probabilistic approach with the intuitive representation of relationships given by graphs. They are composed by two parts: 1. a set X = {X 1 , X 2 , . . . , X p } of random variables describing the quantities
A ModelBased Approach for Distributed User Interfaces. Full paper accepted for EICS’2011
"... This paper describes a modelbased approach for designing distributed user interfaces (DUIs), i.e. graphical user interfaces that are distributed along one or many of the following dimensions: end user, display device, computing platform, and physical environment. The three pillars of this modelbas ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
represent significant distribution states of a DUI and whose transitions are labeled by an evenconditionaction representation. The actions involved in this format may call any distribution primitive of the DUI toolkit. In order to exemplify this modelbased approach, two simple DUIs are first designed: a
Convex relaxation methods for graphical models: Lagrangian and maximum entropy approaches
, 2008
"... Graphical models provide compact representations of complex probability distributions of many random variables through a collection of potential functions defined on small subsets of these variables. This representation is defined with respect to a graph in which nodes represent random variables and ..."
Abstract

Cited by 17 (2 self)
 Add to MetaCart
Graphical models provide compact representations of complex probability distributions of many random variables through a collection of potential functions defined on small subsets of these variables. This representation is defined with respect to a graph in which nodes represent random variables
Kernel Embeddings of Conditional Distributions
"... Many modern applications of signal processing and machine learning, ranging from computer vision to computational biology, require the analysis of large volumes of highdimensional continuousvalued measurements. Complex statistical features are commonplace, including multimodality, skewness, and ..."
Abstract
 Add to MetaCart
of conditional distributions, and provide a novel, unified kernel framework for addressing a broad class of challenging problems in graphical models. The key idea is to map conditional distributions into infinitedimensional feature spaces using kernels, such that subsequent comparisons and manipulations
Observable Operator Processes and Conditioned Continuation Representations
 NEURAL COMPUTATION
, 1997
"... This article introduces observable operator models (OOM) and conditioned continuation representations (CCR). They are tightly interrelated models of certain stationary, finitevalued, discrete, stochastic processes. Both OOM's and CCR's are vector spaces equipped with a set of linear opera ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
This article introduces observable operator models (OOM) and conditioned continuation representations (CCR). They are tightly interrelated models of certain stationary, finitevalued, discrete, stochastic processes. Both OOM's and CCR's are vector spaces equipped with a set of linear
Compatibility of prior specifications across linear models
 Statistical Science
, 2008
"... Abstract. Bayesian model comparison requires the specification of a prior distribution on the parameter space of each candidate model. In this connection two concerns arise: on the one hand the elicitation task rapidly becomes prohibitive as the number of models increases; on the other hand numerous ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
discuss links with related objective approaches. Given an encompassing, or full, model together with a prior on its parameter space, we review and summarize a few procedures for deriving priors under a submodel, namely marginalization, conditioning, and Kullback–Leibler projection. These techniques
Random Effects Graphical Models for Multiple Site Sampling
, 2003
"... We propose a two component graphical chain model, the discrete regression distribution, in which a set of categorical (or discrete) random variables is modeled as a response to a set of categorical and continuous covariates. We examine necessary and sufficient conditions for a discrete regression di ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
We propose a two component graphical chain model, the discrete regression distribution, in which a set of categorical (or discrete) random variables is modeled as a response to a set of categorical and continuous covariates. We examine necessary and sufficient conditions for a discrete regression
Results 11  20
of
640