Based on information theory, we present a method to determine an optimal Markov approximation for modeling and prediction from time series data. The method finds a balance between minimal modeling errors by taking as much as possible memory into account and minimal statistical errors by working in embedding spaces of rather small dimension. A key ingredient is an estimate of the statistical error of entropy estimates. The method is illustrated with several examples, and the consequences for prediction are evaluated by means of the root-mean-squared prediction error for point prediction.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.79.056202 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tumor Markers in Pleural Fluid: A Comprehensive Study on Diagnostic Accuracy.
Diagnostics (Basel)
January 2025
Department of General and Clinical Pathology, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
: Malignant pleural effusions (MPEs) pose a significant challenge in clinical practice and exert a considerable socio-economic burden on the healthcare system, affecting approximately 1 million individuals annually. These effusions are a leading cause of debilitating dyspnea and a diminished quality of life among cancer patients, with distant metastasis to the pleural layers occurring in about 20% of cases during treatment. : A cross-sectional, observational case-control study was conducted on 151 Bulgarian patients with a hydrothorax.
View Article and Find Full Text PDFModelling sensory attenuation as Bayesian causal inference across two datasets.
PLoS One
January 2025
Department of Psychology, Theoretical Cognitive Science Group, Philipps-Universität Marburg, Marburg, Germany.
Introduction: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.
View Article and Find Full Text PDFAdvanced Monte Carlo for Acquisition Sampling in Bayesian Optimization.
Entropy (Basel)
January 2025
Instituto Universitario de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.
Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of learning from previous data, but it typically requires that experiments are sequentially performed. Fully distributed BO addresses the need for efficient parallel and asynchronous active search, especially where traditional centralized BO faces limitations concerning privacy in federated learning and resource utilization in high-performance computing settings.
View Article and Find Full Text PDFEntropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties.
Entropy (Basel)
January 2025
Shandong Rongxin Group Co., Ltd., Zoucheng 273517, China.
In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors.
View Article and Find Full Text PDFA Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm.
Sci Rep
January 2025
Department of Anesthesiology, Changhua Christian Hospital, Changhua, 50050, Taiwan.
In the modern healthcare system, the rational allocation of emergency department (ED) resources is crucial for enhancing emergency response efficiency, ensuring patient safety, and improving the quality of medical services. This paper focuses on the issue of ED resource allocation and designs a priority sorting system for ED patients. The system classifies patients into two queues: urgent and routine.
View Article and Find Full Text PDFWant 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!