ArticleIEEE transactions on signal processing : a publication of the IEEE Signal Processing Society2016
Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective.
Article in IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.Patterns (New York, N.Y.) · 2023Review
- A Nonmathematical Review of Optimal Operator and Experimental Design for Uncertain Scientific Models with Application to Genomics.Current genomics · 2019Review
- An experimental design framework for Markovian gene regulatory networks under stationary control policy.BMC systems biology · 2018Article
- Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks.BMC systems biology · 2018Article
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Authors and funding
2 authors.
Funding
Abstract
The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models.
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Registered trials
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