Bayesian transfer learning between Student-t filters

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00532053" target="_blank" >RIV/67985556:_____/20:00532053 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0165168420301675" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0165168420301675</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sigpro.2020.107624" target="_blank" >10.1016/j.sigpro.2020.107624</a>

Result language

angličtina

Original language name

Bayesian transfer learning between Student-t filters

Original language description

The problem of sequentially transferring a data-predictive probability distribution from a source to a target Bayesian filter is addressed in this paper. In many practical settings, this transfer is incompletely modelled, since the stochastic dependence structure between the filters typically cannot be fully specified. We therefore adopt fully probabilistic design to select the optimal transfer mechanism. We relax the target observation model via a scale-mixing parameter, which proves vital in successfully transferring the first and second moments of the source data predictor. This sensitivity to the transferred second moment ensures that imprecise predictors are rejected, achieving robust transfer. Indeed, Student-t state and observation models are adopted for both learning processes, in order to handle outliers in all hidden and observed variables. A recursive outlier-robust Bayesian transfer learning algorithm is recovered via a local variational Bayes approximation. The outlier rejection and positive transfer properties of the resulting algorithm are clearly demonstrated in a simulated planar position-velocity system, as is the key property of imprecise knowledge rejection (robust transfer), unavailable in current Bayesian transfer algorithms. Performance comparison with particle filter variants demonstrates the successful convergence of our robust variational Bayes transfer learning algorithm in sequential processing.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Signal Processing

ISSN

0165-1684

e-ISSN

—

Volume of the periodical

Volume 175

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

107624

UT code for WoS article

000540817100003

EID of the result in the Scopus database

2-s2.0-85085247688