bibtype V - Research Report
ARLID 0549265
utime 20231122150148.9
mtime 20211207235959.9
title (primary) (eng) Distributed Sequential Zero-Inflated Poisson Regression
publisher
place Praha
name ÚTIA AV ČR, v. v. i.,
pub_time 2021
specification
page_count 11 s.
media_type P
edition
name Research Report
volume_id 2393
keyword Poisson regression
keyword zero inflation
keyword GLM
author (primary)
ARLID cav_un_auth*0247754
name1 Žemlička
name2 R.
country CZ
author
ARLID cav_un_auth*0242543
name1 Dedecius
name2 Kamil
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
country CZ
share 50
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2021/AS/dedecius-0549265.pdf
cas_special
abstract (eng) The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed sequential inference from streaming data in networks with information diffusion. The model is viewed as a probabilistic mixture of a Poisson and a zero-located Dirac component, whose probabilities are estimated using a quasi-Bayesian procedure. The regression coefficients are inferred by means of a weighted Bayesian update. The network nodes share their posterior distributions using the diffusion protocol.\n
RIV BD
FORD0 10000
FORD1 10100
FORD2 10102
reportyear 2022
num_of_auth 2
mrcbC52 4 O 4o 20231122150148.9
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0325721
confidential S
arlyear 2021
mrcbTft \nSoubory v repozitáři: 0549265.pdf
mrcbU10 2021
mrcbU10 Praha ÚTIA AV ČR, v.v.i.,