A probabilistic discrete event system (PDES) is a nondeterministic discrete event system where the probabilities of nondeterministic transitions are specified. State estimation problems of PDES are more difficult than those of non-probabilistic discrete event systems. In our previous papers, we investigated state estimation problems for non-probabilistic discrete event systems. We defined four types of detectabilities and derived necessary and sufficient conditions for checking these detectabilities. In this paper, we extend our study to state estimation problems for PDES by considering the probabilities. The first step in our approach is to convert a given PDES into a nondeterministic discrete event system and find sufficient conditions for checking probabilistic detectabilities. Next, to find necessary and sufficient conditions for checking probabilistic detectabilities, we investigate the "convergence" of event sequences in PDES. An event sequence is convergent if along this sequence, it is more and more certain that the system is in a particular state. We derive conditions for convergence and hence for detectabilities. We focus on systems with complete event observation and no state observation. For better presentation, the theoretical development is illustrated by a simplified example of nephritis diagnosis.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717802 | PMC |
| http://dx.doi.org/10.1016/j.automatica.2008.05.025 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Traditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings . These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups . We introduce a network-based pipeline and visualization tool grounded in relational thinking , which constructs networks from a variety of genetic similarity metrics.
View Article and Find Full Text PDFThe introduction of the SARS-CoV-2 BA.4 lineage into Pakistan.
Arch Virol
January 2025
Department of Virology, National Institute of Health (NIH), 45500, Park Rd, Chak Shahzad, Islamabad, Pakistan.
Pakistan has experienced a total of six COVID-19 waves throughout the pandemic, each driven by distinct SARS-CoV-2 lineages. This study explores the introduction of Omicron lineage BA.4 into Pakistan, which contributed to the sixth wave between June and September 2022.
View Article and Find Full Text PDFA Fast Nonparametric Sampling Method for Time to Event in Individual-Level Simulation Models.
Med Decis Making
January 2025
Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA.
The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.
View Article and Find Full Text PDFEvolution modeling and control of networked dynamic games with event-triggering mechanism.
Chaos
January 2025
School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China.
This paper mainly focuses on investigating the discrete event dynamic decision-making process with two noncooperative intelligent agents, defined as event dynamic games (EDGs). We introduce a novel state space model and analyze the existence of its equilibrium solution. Additionally, we apply principles of network evolution to address the challenge of event dynamic game network modeling.
View Article and Find Full Text PDFAdding automated breast ultrasound to mammography in women with increased breast density or at an elevated risk of breast cancer is a cost-effective screening strategy.
Cancer Causes Control
January 2025
North Valley Breast Clinic, 1335 Buenaventura Blvd, Suite 204, Redding, CA, 96001, USA.
Objectives: Automated breast ultrasound imaging (ABUS) results in a reduction in breast cancer stage at diagnosis beyond that seen with mammographic screening in women with increased breast density or who are at a high risk of breast cancer. It is unknown if the addition of ABUS to mammography or ABUS imaging alone, in this population, is a cost-effective screening strategy.
Methods: A discrete event simulation (Monte Carlo) model was developed to assess the costs of screening, diagnostic evaluation, biopsy, and breast cancer treatment.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!