Special Issue "Algorithms for Sequential Analysis"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: 15 May 2021.

Special Issue Editor

Dr. Georgy Sofronov
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
Interests: statistical modeling; change-point problem; Markov chain Monte Carlo methods; cross-Entropy method; optimal stopping rules

Special Issue Information

Dear Colleagues,

In many applications, it is necessary to make decisions while information is still being collected. Decision-makers regularly face such problems in important areas including cyber risk, resource allocations, and finance. The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in algorithms for sequential analysis. This Special Issue provides a forum for academics and practitioners to disseminate high-quality results related to theoretical and practical aspects of sequential algorithms. Potential topics include, but are not limited to, dynamic programming, online machine learning algorithms, Monte Carlo methods in sequential analysis, Markov decision processes, Bayesian sequential analysis, optimal stopping rules, quickest change-point detection problem, and stochastic games.

Dr. Georgy Sofronov
Guest Editor

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. Algorithms is an international peer-reviewed open access monthly 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 1400 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.

Keywords

  • Dynamic programming
  • Optimal decision making
  • Sequential data analysis
  • Bayesian sequential analysis
  • Online machine learning
  • Reinforcement learning
  • Q-learning
  • Least square Monte Carlo
  • Quickest change-point problem
  • Optimal stopping

Published Papers (3 papers)

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Research

Open AccessArticle
Perpetual American Cancellable Standard Options in Models with Last Passage Times
Algorithms 2021, 14(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010003 - 24 Dec 2020
Viewed by 512
Abstract
We derive explicit solutions to the perpetual American cancellable standard put and call options in an extension of the Black–Merton–Scholes model. It is assumed that the contracts are cancelled at the last hitting times for the underlying asset price process of some constant [...] Read more.
We derive explicit solutions to the perpetual American cancellable standard put and call options in an extension of the Black–Merton–Scholes model. It is assumed that the contracts are cancelled at the last hitting times for the underlying asset price process of some constant upper or lower levels which are not stopping times with respect to the observable filtration. We show that the optimal exercise times are the first times at which the asset price reaches some lower or upper constant levels. The proof is based on the reduction of the original optimal stopping problems to the associated free-boundary problems and the solution of the latter problems by means of the smooth-fit conditions. Full article
(This article belongs to the Special Issue Algorithms for Sequential Analysis)
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Open AccessArticle
Feasibility of Kd-Trees in Gaussian Process Regression to Partition Test Points in High Resolution Input Space
Algorithms 2020, 13(12), 327; https://0-doi-org.brum.beds.ac.uk/10.3390/a13120327 - 05 Dec 2020
Viewed by 701
Abstract
Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we [...] Read more.
Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we want to make predictions, not the set of training points in the dataset) by a kd-tree, a multi-resolution data structure arises that allows for considerable gains in performance and memory usage without a significant loss of accuracy. In this paper, we study the feasibility and efficiency of constructing and using such a kd-tree in Gaussian process regression. We propose a cut-off rule that is easy to interpret and to tune. We show our findings on generated toy data in a 3D point cloud and a simulated 2D vibrometry example. This survey is beneficial for researchers that are working on a high resolution input space. The kd-tree approximation outperforms the naïve Gaussian process implementation in all experiments. Full article
(This article belongs to the Special Issue Algorithms for Sequential Analysis)
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Open AccessArticle
Cross-Entropy Method in Application to the SIRC Model
Algorithms 2020, 13(11), 281; https://0-doi-org.brum.beds.ac.uk/10.3390/a13110281 - 06 Nov 2020
Viewed by 514
Abstract
The study considers the usage of a probabilistic optimization method called Cross-Entropy (CE). This is the version of the Monte Carlo method created by Reuven Rubinstein (1997). It was developed in the context of determining rare events. Here we will present the way [...] Read more.
The study considers the usage of a probabilistic optimization method called Cross-Entropy (CE). This is the version of the Monte Carlo method created by Reuven Rubinstein (1997). It was developed in the context of determining rare events. Here we will present the way in which the CE method can be used for problems of optimization of epidemiological models, and more specifically the optimization of the Susceptible–Infectious–Recovered–Cross-immune (SIRC) model based on the functions supervising the care of specific groups in the model. With the help of weighted sampling, an attempt was made to find the fastest and most accurate version of the algorithm. Full article
(This article belongs to the Special Issue Algorithms for Sequential Analysis)
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