| 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., |
|