Add to ORKG\u3cp\u3eThis paper describes an Imprecise Dirichlet Model and the maximum entropy criterion to learn Bayesian network parameters under insufficient and incomplete data. The method is applied to two distinct recognition problems, namely, a facial action unit recognition and an activity recognition in video surveillance sequences. The model treats a wide range of constraints that can be specified by experts, and deals with incomplete data using an ad-hoc expectation- maximization procedure. It is also described how the same idea can be used to learn dynamic Bayesian networks. With synthetic data, we show that our proposal and widely used methods, such as the Bayesian maximum a posteriori, achieve similar accuracy. However, when real data com...
This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Di...
Purely data-driven methods often fail to learn accurate conditional probability table (CPT) paramete...
We propose an efficient family of algorithms to learn the parameters of a Bayesian network from inco...
\u3cp\u3eThis chapter addresses the problem of estimating the parameters of a Bayesian network from ...
This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The ta...
\u3cp\u3eProbabilistic graphical models such as Bayesian Networks have been increasingly applied to ...
We propose a family of efficient algorithms for learning the parameters of a Bayesian network from i...
Learning from data ranges between extracting essentials from the data, to the more fundamental and v...
A classic approach for learning Bayesian networks from data is to select the maximum a posteriori (M...
\u3cp\u3eThis paper describes a new approach to unify constraints on parameters with training data t...
Bayesian networks, which provide a compact graphical way to express complex probabilistic relationsh...
We propose an efficient family of algorithms to learn the parameters of a Bayesian network from inco...
A classic approach for learning Bayesian networks from data is to identify a maximum a posteriori (M...
This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Di...
When the historical data are limited, the conditional probabilities associated with the nodes of Bay...
This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Di...
Purely data-driven methods often fail to learn accurate conditional probability table (CPT) paramete...
We propose an efficient family of algorithms to learn the parameters of a Bayesian network from inco...
\u3cp\u3eThis chapter addresses the problem of estimating the parameters of a Bayesian network from ...
This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The ta...
\u3cp\u3eProbabilistic graphical models such as Bayesian Networks have been increasingly applied to ...
We propose a family of efficient algorithms for learning the parameters of a Bayesian network from i...
Learning from data ranges between extracting essentials from the data, to the more fundamental and v...
A classic approach for learning Bayesian networks from data is to select the maximum a posteriori (M...
\u3cp\u3eThis paper describes a new approach to unify constraints on parameters with training data t...
Bayesian networks, which provide a compact graphical way to express complex probabilistic relationsh...
We propose an efficient family of algorithms to learn the parameters of a Bayesian network from inco...
A classic approach for learning Bayesian networks from data is to identify a maximum a posteriori (M...
This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Di...
When the historical data are limited, the conditional probabilities associated with the nodes of Bay...
This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Di...
Purely data-driven methods often fail to learn accurate conditional probability table (CPT) paramete...
We propose an efficient family of algorithms to learn the parameters of a Bayesian network from inco...