| bibtype |
D -
Thesis
|
| ARLID |
0598658 |
| utime |
20241010093514.8 |
| mtime |
20240926235959.9 |
| title
(primary) (eng) |
Pathwise Duality of Interacting Particle Systems |
| publisher |
| place |
Praha |
| name |
Katedra pravděpodobnosti a matematické statistiky MFF UK |
| pub_time |
2024 |
|
| specification |
| page_count |
138 s. |
| media_type |
P |
|
| keyword |
pathwise duality |
| keyword |
interacting particle systems |
| keyword |
monotone Markov process |
| keyword |
monoid |
| keyword |
module |
| author
(primary) |
| ARLID |
cav_un_auth*0450907 |
| name1 |
Latz |
| name2 |
Jan Niklas |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| country |
NL |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
SVV 260701 |
| agency |
GA UK |
| country |
CZ |
| ARLID |
cav_un_auth*0473203 |
|
| project |
| project_id |
GA20-08468S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0397552 |
|
| abstract
(eng) |
In the study of Markov processes, duality is an important tool used to prove various types of long-time behavior. Nowadays, there exist two predominant approaches to Markov process duality: the algebraic one and the pathwise one. This thesis utilizes the pathwise approach in order to identify new dualities of interacting particle systems and to present previously known dualities within a unified framework. Three classes of pathwise dualities are identified by equipping the state space of an interacting particle system with the additional structure of a monoid, a module over a semiring, and a partially ordered set, respectively. This additional structure then induces a pathwise duality for each interacting particle system that preserves this structure in the sense that its generator can be written using only structure-preserving local maps. |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2025 |
| habilitation |
| degree |
Ph.D. |
| institution |
Katedra pravděpodobnosti a matematické statistiky MFF UK |
| place |
Praha |
| year |
2024 |
| dates |
24.9.2024 |
|
| num_of_auth |
1 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0356717 |
| cooperation |
| ARLID |
cav_un_auth*0372408 |
| name |
Karlova Universita v Praze |
| country |
CZ |
|
| confidential |
S |
| arlyear |
2024 |
| mrcbU10 |
2024 |
| mrcbU10 |
Praha Katedra pravděpodobnosti a matematické statistiky MFF UK |
|