Bayesian transfer learning between uniformly modelled Bayesian filters

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00537103" target="_blank" >RIV/67985556:_____/21:00537103 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-63193-2_9" target="_blank" >http://dx.doi.org/10.1007/978-3-030-63193-2_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63193-2_9" target="_blank" >10.1007/978-3-030-63193-2_9</a>

Result language

angličtina

Original language name

Bayesian transfer learning between uniformly modelled Bayesian filters

Original language description

We investigate sensor network nodes that sequentially infer states with bounded values, and affected by noise that is also bounded. The transfer of knowledge between such nodes is the principal focus of this chapter. A fully Bayesian framework is adopted, in which the source knowledge is represented by a bounded data predictor, the specification of a formal conditioning mechanism between the filtering nodes is avoided, and the optimal knowledge-conditioned target state predictor is designed via optimal Bayesian decision-making (fullynprobabilistic design). We call this framework Bayesian transfer learning, and derive a sequential algorithm for pairs of interacting, bounded filters. To achieve a tractable, finite-dimensional flow, the outputs of the time step, transfer step and data step are locally projected onto parallelotopic supports. An informal variant of the transfer algorithm demonstrates both strongly positive transfer of high-quality (low variance) source knowledge--improving on a former orthotopically supported variant--as well as rejection of low-quality (high variance) source knowledge, which we call robust transfer.

Czech name

—

Czech description

—

Type

C - Chapter in a specialist book

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Book/collection name

Informatics in Control, Automation and Robotics : 16th International Conference, ICINCO 2019 Prague, Czech Republic, July 29-31, 2019, Revised Selected Papers

ISBN

978-3-030-63192-5

Number of pages of the result

18

Pages from-to

151-168

Number of pages of the book

193

Publisher name

Springer

Place of publication

Cham

UT code for WoS chapter

—