paper-with-me

Papers

Probability Estimation of Uncertain Process Trace Realizations

2021-08-19 · Marco Pegoraro, Bianka Bakullari, Merih Seran Uysal, Wil M. P. van der Aalst

Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the probability of each of such scenarios, allowing their analysis. Experiments show that the probabilities calculated with our method closely match the true chances of occurrence of specific outcomes, enabling more trustworthy analyses on uncertain data.

📄 PDF Abstract BibTeX arXiv:2108.08615

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A Theory of Uncertainty Variables for State Estimation and Inference

2019-09-24 · Rajat Talak, Sertac Karaman, Eytan Modiano

We develop a new framework of uncertainty variables to model uncertainty. An uncertainty variable is characterized by an uncertainty set, in which its realization is bound to lie, while the conditional uncertainty is cha…

State Estimation

Safe learning-based control via function-based uncertainty quantification

2026-04-01 · Abdullah Tokmak, Toni Karvonen, Thomas B. Schön, Dominik Baumann arxiv

Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by constructing uncertainty tubes that enclose the unknown function of interest,…

A Moreau Envelope Approach for LQR Meta-Policy Estimation

2024-03-26 · Ashwin Aravind, Mohammad Taha Toghani, César A. Uribe

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a f…

Meta-Learning

Frequency-domain Gaussian Process Models for $H_\infty$ Uncertainties

2023-12-15 · Alex Devonport, Peter Seiler, Murat Arcak

Complex-valued Gaussian processes are commonly used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\infty$ function with probability on…

Gaussian Processes

Distributionally Robust Markov Decision Processes

2010-12-01 · NeurIPS 2010 12 · Huan Xu, Shie Mannor

We consider Markov decision processes where the values of the parameters are uncertain. This uncertainty is described by a sequence of nested sets (that is, each set contains the previous one), each of which corresponds …