Special Issue "Probability, Stochastic Processes and Optimization"
Deadline for manuscript submissions: 28 March 2022.
Select the Journal "Mathematics" and submit your paper to the Special Issue "Probability, Stochastic Processes and Optimization" via: https://susy.mdpi.com/user/manuscripts/upload?journal=mathematics. Please contact the journal editor Helene Hu ([email protected]) for any queries.
2. Institute of Mathematical Statistics and Applied Mathematics, 050711 Bucharest, Romania
Interests: stochastic processes; evolutionary algorithms; cellular automata
Interests: simulation methods; Monte Carlo method; statistical learning; data mining; artificial intelligence; biostatistics
Interests: markov and semi-Markov processes; hidden Markov and hidden semi-Markov processes; statistical inference for stochastic processes; parametric and nonparametric estimation; hypotheses testing; stochastic methods in reliability and survival analysis; biostatistics; stochastic methods for DNA modelling; entropy and divergence measures; model selection
Special Issues and Collections in MDPI journals
In applied research, Probability Theory is usually regarded as a distant, often neglected relative of Statistics. We try to rectify this misconception by publishing papers underlying new applications and original models for real-world problems, from either natural, computer or social sciences that are based on Probability and Stochastic Processes.
The suggested, yet not restrictive, topics include the following:
- Cellular automata;
- Bayes analysis;
- Markov processes and related topics;
- Hidden Markov processes;
- Applications of stochastic processes in Economics, Finance, Social Sciences, etc.;
- Stochastic modeling;
- Measures of divergence and entropy;
- Monte Carlo simulations.
Prof. Dr. Alexandru Agapie
Prof. Dr. Denis Enachescu
Dr. Vlad Stefan Barbu
Prof. Dr. Bogdan Iftimie
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Probabilistic optimization
- Evolutionary algorithms
- Stochastic modeling
- Probabilistic cellular automata
- Markov processes and related topics in social sciences
- Hidden Markov chains
- Markov chains for learning human behavior
- Stochastic processes and their applications in economics
- Monte Carlo methods
- Bayesian analysis
- Bayesian networks
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: On Spherical Distributions used in Evolutionary Algorithms
Authors: Alexandru AGAPIE
Affiliation: Bucharest University of Economic Studies and Institute of Mathematical Statistics and Applied Mathematics Bucharest
Abstract: Convergence analysis of continuous evolutionary algorithms relies on the probabilistic analysis of local quantities like success probability and expected progress. An analytic description of these quantities is possible, yet calculus is usually intractable. Uniform distribution inside the sphere is a multi-dimensional random variable with dependent components, which proves to be an alternative to the commonly used normal multi-variate distribution, with independent components. The paper demonstrates some properties of the one-step expected progress of the evolutionary algorithm with one individual, mutation operator and minimizing the SPHERE fitness function.
Title: Does the lack of supporters affect the home team advantage in football matches? The COVID-19 pandemic case
Authors: Bogdan ALEXE; Denis ENACHESCU
Affiliation: Institute of Mathematical Statistics and Applied Mathematics Bucharest
Abstract: The home team advantage plays an important role in the performance of a football team. Statistically, teams playing at home score more goals and concede less, consequently, earning more points and performing better than teams playing away. One of the many factors influencing the home team advantage is the support of the home crowd. The COVID-19 pandemic has changed the conditions in which football matches are played with supporters being partially or totally banned in attending football matches. In this paper we tray to give a statistical answer if the lack of supporters affects the home team advantage in football matches. We based our studies on data containing football matches from the most known European leagues (England, Spain, Germany, Italy) played before and after the COVID-19 pandemic emerged.
Title: A mathematical optimization model with input data represented by triangular fuzzy numbers
Authors: Marius GIUCLEA; Costin-Ciprian POPESCU
Affiliation: Bucharest University of Economic Studies
Abstract: By extending some optimization models usually used for data in the form of real numbers, so that they can be used for fuzzy numbers, their adaptability to various situations increases significantly. This paper analyzes a mathematical optimization model for which the input data are represented by triangular fuzzy numbers instead of real numbers. Certain concepts specific to fuzzy numbers, such as the distance between two fuzzy numbers, are discussed and used.
Title: A new central limit theorem for Kolmogorov means
Authors: Simona COJOCEA
Affiliation: University of Bucharest, Doctoral School in Mathematics
Abstract: In his paper from 1930 “Sur la notion de la moyenne” Kolmogorov introduces a generalized mean which was compatible with the arithmetic mean, the geometric mean and the harmonic mean. This was only the starting point of further generalization, so many new classes of generalized means have emerged. In this paper, we take a look at Kolmogorov’s original paper with a fresh perspective and we present a central limit theorem using the Kolmogorov expected value for a particular class of random variables.
Title: An Intrinsic Entropy-based Stock Market Cross-sectional Uncertainty Estimation
Authors: Claudiu VINTE 1; Marcel AUSLOOS 2; Bogdan IFTIMIE 1
Affiliation: 1. Bucharest University of Economic Studies 2. School of Business, Brookfield, University of Leicester
Abstract: Uncertainty in stock market is intimately related to time. The temporal dimension of uncertainty has been traditionally embedded in the volatility estimation of a given exchange-traded security over a certain time frame. This paper introduces a novel cross-sectional estimation of stock market uncertainty based on the intrinsic entropy model. The proposed intrinsic entropy-based estimation takes into account the historical daily traded prices, namely open, high, low, and close prices (OHLC), along with the daily traded volume for each and all the listed titles on The New York Stock Exchange (NYSE). We conduct a comparative analysis between the intrinsic entropy time series obtained from the cross- sectional model against the intrinsic entropy-based volatility estimation computed for the S&P 500 index in the same time interval. A GARCH model is employed in order to assess the predictive power of the market cross-sectional uncertainty estimation in comparison with the intrinsic entropy-based volatility estimation of the S&P 500 index.