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 |
|
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., |
|