Deep learning-based estimation of time-dependent parameters in Markov models with application to nonlinear regression and SDEs

1 code implementation • 13 Dec 2023 • Andrzej Kałuża, Paweł M. Morkisz, Bartłomiej Mulewicz, Paweł Przybyłowicz, Martyna Wiącek

We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling.

regression

On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland

no code implementations • 26 Apr 2022 • Piotr Błaszczyk, Konrad Klimczak, Adam Mahdi, Piotr Oprocha, Paweł Potorski, Paweł Przybyłowicz, Michał Sobieraj

We propose a novel methodology for estimating the epidemiological parameters of a modified SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus spread.

Existence and uniqueness of solutions of SDEs with discontinuous drift and finite activity jumps

no code implementations • 5 Oct 2020 • Paweł Przybyłowicz, Michaela Szölgyenyi, Fanhui Xu

In this letter we prove existence and uniqueness of strong solutions to multi-dimensional SDEs with discontinuous drift and finite activity jumps.

Probability 60H10

Existence, uniqueness, and approximation of solutions of jump-diffusion SDEs with discontinuous drift

no code implementations • 9 Dec 2019 • Paweł Przybyłowicz, Michaela Szölgyenyi

In this paper we study jump-diffusion stochastic differential equations (SDEs) with a discontinuous drift coefficient and a possibly degenerate diffusion coefficient.

Numerical Analysis Numerical Analysis Probability 60H10, 65C30, 65C20, 65L20