#### Filter Results:

- Full text PDF available (13)

#### Publication Year

2002

2016

- This year (0)
- Last 5 years (1)
- Last 10 years (2)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Jaime Shinsuke Ide, Fábio Gagliardi Cozman
- SBIA
- 2002

This paper presents new methods for generation of random Bayesian networks. Such methods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the “structure” of the network) and then,… (More)

We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples. The introduction of constraints on induced width leads to realistic networks but requires new techniques. A tool that generates random networks is presented and applications are… (More)

This paper presents two approximate algorithms for inference in graphical models for binary random variables and imprecise probability. Exact inference in such models is extremely challenging in multiply-connected graphs. We describe and implement two new approximate algorithms. The first one is the Iterated Partial Evaluation (IPE) algorithm, directly… (More)

- Jaime Shinsuke Ide, Fábio Gagliardi Cozman
- Int. J. Approx. Reasoning
- 2008

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief… (More)

- Fabio Augusto Menocci Cappabianco, Claudio Saburo Shida, Jaime Shinsuke Ide
- SIBGRAPI Tutorials
- 2016

An important aspect of probabilistic inference in embedded real-time systems is flexibility to handle changes and limitations in space and time resources. We present algorithms for probabilistic inference that focus on simultaneous adaptation with respect to these resources. We discuss techniques to reduce memory consumption in Bayesian network inference,… (More)

- Jaime Shinsuke Ide, Fábio Gagliardi Cozman
- ISIPTA
- 2005

Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution for large networks. This paper introduces a variational approach to approximate inference in credal networks: we show how to formulate mean field approximations using naive (fully… (More)

Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity, but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, polytree-shaped credal networks can be efficiently updated by the 2U algorithm. In this paper we present a binarization algorithm, that… (More)

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through… (More)

We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples, building upon previous algorithms by the authors. The introduction of constraints on induced width leads to more realistic results but requires new techniques. We discuss three… (More)