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Probabilistic Graphical Models Probabilistic Graphical Models
"... Abstract This report 1 presents probabilistic graphical models that are based on imprecise probabilities using a comprehensive language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform infer ..."
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Abstract This report 1 presents probabilistic graphical models that are based on imprecise probabilities using a comprehensive language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform
Probabilistic Graphical Models
, 2014
"... This report presents probabilistic graphical models that are based on imprecise probabilities using a simplified language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform inference, and disc ..."
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Cited by 6 (2 self)
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This report presents probabilistic graphical models that are based on imprecise probabilities using a simplified language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform inference
for probabilistic graphical models
"... Carmen is an open-source software package for probabilistic graphical models (PGMs), which aims at being useful for different research groups and for building real-world applications. After reviewing similar projects launched in the last years, we analyze the general properties of Carmen and how it ..."
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Carmen is an open-source software package for probabilistic graphical models (PGMs), which aims at being useful for different research groups and for building real-world applications. After reviewing similar projects launched in the last years, we analyze the general properties of Carmen and how
Functions on Probabilistic Graphical Models
"... Abstract—Probabilistic graphical models are tools that are used to represent the probability distribution of a vector of random variables X = (X1,..., XN). In this paper we introduce functions f(x1,..., xN) defined over the given vector. These functions also are random variables. The main result of ..."
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Abstract—Probabilistic graphical models are tools that are used to represent the probability distribution of a vector of random variables X = (X1,..., XN). In this paper we introduce functions f(x1,..., xN) defined over the given vector. These functions also are random variables. The main result
Harmonic Analysis with Probabilistic Graphical Models
, 2003
"... A technique for harmonic analysis is presented that partitions a piece of music into contiguous regions and labels each with the key, mode, and functional chord, e.g. tonic, dominant, etc. The analysis is performed with a hidden Markov model and, as such, is automatically trainable from generic MIDI ..."
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Cited by 28 (2 self)
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MIDI files and capable of finding the globally optimal harmonic labeling. Experiments are presented highlighting our current state of the art. An extension to a more complex probabilistic graphical model is outlined in which music is modeled as a collection of voices that evolve independently given
Abductive Inference with Probabilistic Graphical Models
"... Starting from a general characterization of logical inferences, I consider abductive reasoning, which aims at finding likely causes for observed symptoms. Such inferences are not truth preserving and thus it is necessary to assess their conclusions, to compare different explanations of the same fin ..."
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findings, and finally to select the “best ” hypothesis. Since in a large number of applications probabilistic graphical models are a mathematically sound and also very convenient tool for these operations, I discuss how they can be used to make abductive inference feasible.
Lipschitz Parametrization of Probabilistic Graphical Models
"... We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the ℓp-norm of the parameters. We discuss several implications of Lipschitz parametrization. We present an upper bound of the Kullback-Leibler divergence that allows understanding method ..."
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Cited by 4 (0 self)
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We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the ℓp-norm of the parameters. We discuss several implications of Lipschitz parametrization. We present an upper bound of the Kullback-Leibler divergence that allows understanding
Probabilistic Graphical Models and their Role in Databases
"... Probabilistic graphical models provide a framework for compact representation and efficient reasoning about the joint probability distribution of several interdependent variables. This is a classical topic with roots in statistical physics. In recent years, spurred by several applications in unstruc ..."
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Cited by 4 (0 self)
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Probabilistic graphical models provide a framework for compact representation and efficient reasoning about the joint probability distribution of several interdependent variables. This is a classical topic with roots in statistical physics. In recent years, spurred by several applications
A probabilistic graphical model of quantum systems
"... Abstract—Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered. To integrate the distributed information we propose a quantum version of probabilistic grap ..."
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graphical models. Variables in the model (quantum states and measurement outcomes) are linked by several types of operators (unitary, measurement, and merge/split operators). We propose algorithms for three machine learning tasks in quantum probabilistic graphical models: a belief propagation algorithm
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45,096
