Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances
Result description
This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.
Keywords
Knowledge fusionBayesian transfer learningFully probabilistic designState–space modelsBounded noiseBayesian inference
The result's identifiers
Result code in IS VaVaI
Result on the web
https://www.sciencedirect.com/science/article/pii/S0950705121010388
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances
Original language description
This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.
Czech name
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Czech description
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Classification
Type
JSC - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Knowledge-Based System
ISSN
0950-7051
e-ISSN
1872-7409
Volume of the periodical
238
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
107879
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85122230192
Basic information
Result type
JSC - Article in a specialist periodical, which is included in the SCOPUS database
OECD FORD
Statistics and probability
Year of implementation
2022