Sequential updating of conditional probabilities on directed graphical structures

Spiegelhalter, David; Lauritzen, Steffen

doi:10.1002/net.3230200507

Cited by 392 publications

(203 citation statements)

References 17 publications

(28 reference statements)

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“…Pearl [36] noted that simulation methods could also be used for approximate inference in directed graphical models, and Spiegelhalter and Lauritzen [37] showed that graphical models could also include parameters as nodes, hence facilitating general Bayesian inference. It became apparent that, by merging these ideas, the computations required for Bayesian inference were simply an iterative sequence of local calculations on the graph, and that each such calculation, viewed abstractly, was identical.…”

Section: Inception and Early Yearsmentioning

confidence: 99%

The BUGS project: Evolution, critique and future directions

Lunn

Spiegelhalter

Thomas

et al. 2009

Statistics in Medicine

1,705

1,312

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SUMMARYBUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.

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Section: Inception and Early Yearsmentioning

confidence: 99%

The BUGS project: Evolution, critique and future directions

Lunn

Spiegelhalter

Thomas

et al. 2009

Statistics in Medicine

1,705

1,312

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“…The observed variables are represented by the evidence vector E. The inference algorithm updates the belief given E on all stochastic variables P(U i |E). These values can be used to update the probabilities P(U i ) using a process known as sequential updating [13,19,20]:…”

Section: Bayesian Decision Networkmentioning

confidence: 99%

Scheduling Under Conditions of Uncertainty: A Bayesian Approach

Santos

Proença

2004

Lecture Notes in Computer Science

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Abstract. The efficient execution of irregular parallel applications on shared distributed systems requires novel approaches to scheduling, since both the application requirements and the system resources exhibit an unpredictable behavior. This paper proposes Bayesian decision networks as the paradigm to handle the uncertainty a scheduler has about the environment's current and future states. Experiments performed with a parallel ray tracer show promising performance improvements over a deterministic approach of identical complexity. These improvements grow as the level of system sharing and the application's workload irregularity increase, suggesting that the effectiveness of decision network based schedulers grows with the complexity of the environment being managed.

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“…Bayesian network structuring algorithms include those based on maximum likelihood principles [22], minimum cross-entropy [23], minimum description length (MDL) [24], Akaike information criteria (AIC), and the Bayesian information criteria (BIC) [25]. The strategies for learning the conditional probabilities of the Bayesian network include approaches based on the principle of maximum likelihood (ML) [26], maximum a posterior probability (MAP) [27], and incremental learning [28].…”

Section: Learning Pdn Structure and Probabilitiesmentioning

confidence: 99%

Modeling Parameter Space Behavior of Vision Systems Using Bayesian Networks

Sarkar

Chavali

2000

Computer Vision and Image Understanding

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The performance of most vision systems (or subsystems) is significantly dependent on the choice of its various parameters or thresholds. The associated parameter search space is extremely large and nonsmooth; moreover, the optimal choices of the parameters are usually mutually dependent on each other. In this paper we offer a Bayesian network-based probabilistic formalism, which we call the parameter dependence networks (PDNs), to model, abstract, and analyze the parameter space behavior of vision systems. The various algorithm parameters are the nodes of the PDN and are associated with probabilistic beliefs about the optimality of their respective values. The links between the nodes capture the direct dependencies between them and are quantified by conditional belief functions. The PDN structure captures the interdependence among the parameters in a concise and explicit manner. We define information theoretic measures, based on these PDNs, to quantify the global parameter sensitivity and the strength of the interdependence of the parameters. These measures predict the general ease of parameter tuning and performance stability of the system. The PDNs can also be used to stochastically sample the parameter space, to select optimal parameter sets (e.g., in performance evaluation studies), and to choose parameters, given constraints on the choice of some parameters. We also offer a strategy based on stochastic learning automata to generate training data to automatically build these PDNs. The team of learning automata stochastically samples the parameter space in a nonuniform manner with more samples near optimal values. These nonuniform samples are used to infer both the dependency structure and the conditional probabilities of the PDN. We demonstrate the process of construction of the PDN for an isolated vision module with 4 parameters (an edge detector), a coupling of two vision modules with a total of 7 parameters (a small edge grouping module), and a combination of three vision modules with 21 parameters (a complex perceptual organization module).

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Sequential updating of conditional probabilities on directed graphical structures

Cited by 392 publications

References 17 publications

The BUGS project: Evolution, critique and future directions

The BUGS project: Evolution, critique and future directions

Scheduling Under Conditions of Uncertainty: A Bayesian Approach

Modeling Parameter Space Behavior of Vision Systems Using Bayesian Networks

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