Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) mission (http://www.nssc.cas.cn/smile/) is a scientific space mission, jointly supported by the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS). SMILE aims to study the solar wind-magnetosphere coupling in a novel approach: global imaging of the Earth’s magnetosheath and cusps in the soft X-ray band. At the same time, it also provides UV imaging of the northern auroral regions, completing the detection of solar wind–magnetosphere–ionosphere coupling in a global way. The time frame for launch is in 2025. This talk summarizes the recent progress of SMILE Modeling Working Group (MWG), specifically a special issue on Earth and Planetary Physics (EPP) with 23 articles to provide the international space science community with works regarding the modeling and data analysis methods developed during the pre-studies of the SMILE mission. We categorize the articles into the following topics and give some brief comments: (1) instrument descriptions of the Soft X-ray Imager (SXI), (2) modeling of the X-ray signals, (3) data processing of the images, (4) tracing the boundary locations from the simulated images, (5) physical phenomenon related to the scientific goals of SMILE-SXI, (6) modeling of the aurora, and (7) ground-based support for SMILE.

The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this purpose, but since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small data sets. In this paper, we present a novel method for GPs modeling in one dimension where the computational requirements scale linearly with the size of the data set. We demonstrate the method by applying it to simulated and real astronomical time series data sets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically driven damped harmonic oscillators—providing a physical motivation for and interpretation of this choice—but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable GP methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.

Among the methods proposed for identifying the number of latent traits in multidimensional IRT models, DETECT has attracted the attention of both methodologists and applied researchers as a nonparametric counterpart to other procedures. The current study investigated the overall performance of the DETECT procedure and its outcomes using a real-data sampling design recommended by MacCallum (2003) and compared the results from a purely simulated data set that was generated with a well-specified “perfect” model. The comparison revealed that the sampling behavior of the maximized DETECT value and R-ratio statistics was quite robust to minor factors and other model misspecifications that potentially exist in the real data set, as there were negligible differences between the results of the real and simulated data sets. Item classification accuracy was also nearly identical for the real and simulated data sets. The accuracy of the identified number of dimensions reported by DETECT was the only outcome with an obvious difference between the purely simulated data set and the real data set. While the difference was small for smaller sample sizes, the identified number of dimensions was more accurate for larger sample sizes when the population data set was purely simulated. In many instances, exploratory DETECT analysis outperformed the cross-validated DETECT analysis in terms of overall accuracy.

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling ToolsIncorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make o

Extensive Copy-Number Variation of the Human Olfactory Receptor Gene Family

This paper discusses educational tools, resources and experience in teaching physics-of-failure (PoF) modeling and data analysis methods used in accelerated life testing (ALT) and accelerated degradation testing (ADT). The concepts introduced in this paper include several probabilistic and highly technical approaches such as the PoF and mechanistic approaches to reliability prediction and assessments as well as advanced Bayesian analysis methods used in ALT and ADT to analyze life data obtained through various methods of constant and variable stress testing efforts. Other resources include codes and algorithms from the readily available computational tools R, MATLAB, and WinBUGS for solving advanced model and life estimation methods related to PoF, ALT and ADT analyses. This paper provides an overview of these resources and several example problems.

Small unoccupied aerial systems (sUAS) are increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, precise manoeuvrability, and navigation makes them versatile tools. The goal of this study is to investigate if sUAS multispectral imagery can be utilized to measure turbidity and total suspended solids (TSS) of small streams. sUAS multispectral imagery and water samples at varying depths were collected before and after rain events on three sampling dates in 2019 from Moores Creek in Lanett, Alabama (AL), United States of America (USA), which was restored in 2017. The water samples were processed for TSS and turbidity and related to pixel values from the multispectral imagery. Linear regression was used to develop models for TSS and turbidity. The models were then tested on Moores Mill Creek in Chewacla State Park, AL, USA. For Lanett, TSS and turbidity regression models for low flows had coefficients of determination (R 2) values of 0.77 and 0.78, respectively. During high flows, different single bands and band ratios were required for comparable R 2 values, suggesting separate models may be needed for high and low flow events. When the Lanett models were applied to Chewacla State Park, predicted TSS and turbidity were not comparable to measured values indicating that location-specific models may be required. Future research should incorporate depth as a variable since streambed visibility likely impacts results, along with other modelling and data analysis methods, such as machine learning.

Simultaneous cancer classification and gene selection with Bayesian nearest neighbor method: An integrated approach

Recently, there has been an explosive growth in the development of the concept of Industry. One of the most important elements of this concept is the application of methods of mathematical modeling and intelligent data analysis to create models of production processes and final products based on the processing of signals coming from intelligent sensors. A set of such models, which represent a virtual representation of industrial processes, systems and equipment, is called twin. Digital twins use data from sensors installed on production lines or on the basis of final products to predict equipment failures, optimize product quality and reduce the environmental impact of production processes. The model sets, underlying digital twins, can be described as workflows consisting of a set of computational services, each of which is a model of a process step. To provide flexible support for cloud computing to perform digital twins, we offer the concept of Micro-Workflows, which combines the power of the Scientific Workflows concept, the flexibility of container technology and the sustainability of the Stream Processing approach in distributed computing systems.

In recent years, text mining has become a branch of Natural Language Processing. This chapter focuses on the analysis of textual data, illustrated by examples treated with the software R, which will be used to manipulate texts and strings, to transform them by word embedding methods and to analyse them by supervised and unsupervised statistical and machine learning methods. It discusses methods of topic modeling and sentiment analysis. There are two types of methods in textual data analysis: descriptive methods and predictive methods. The chapter describes the various lexical analyses that are often performed to categorize documents or to detect the topics contained in a set of documents. The analysis of textual data is most often done on vector representations of the data, which are stored in matrices containing the documents in rows and the terms in columns. A recent application of natural language processing is sentiment analysis.

Introduction. The development and enhancement of the management efficiency of the forestry sector in the Macroregion of the Northwest and the Arctic zone of the Russian Federation are strategically significant for the country's development, oriented towards global competitiveness. The aim of this study is to analyze the prospects for personnel training within the system of secondary vocational education for the forestry sector in the Northwest and Arctic zones of the Russian Federation. Materials and Methods. The materials for the study were statistical data from two forms of state federal reporting for 2022: No. 1-T (prof) "Information on the number and demand for workers in organizations by professional groups" and form No. SPO-1 "Information about the educational organization that carries out educational activities in educational programs of secondary vocational education." The study employed methods of descriptive statistics, economic-mathematical modeling, and comparative data analysis. Results of the Study. An extended list of 23 directions and specialties of secondary vocational education organizations training qualified personnel for the forestry complex in the Arctic Macroregion and Northwest Russia has been identified. An assessment of the provision of the economy of enterprises and organizations in the forestry sector with qualified personnel has been presented. The analysis revealed 12 entities in the macroregion whose personnel needs for mid-level specialists can be met by graduates from their own region, as well as 13 entities in the macroregion that need to train their personnel beyond the macroregion in other regions of Russia to meet the demand for qualified workers and employees. A mapping tool has been developed, comprising 85 subjects of the Russian Federation, divided into three groups based on the total admission to specialties and directions related to the forestry sector. Discussion and Conclusion. The mapping tool obtained demonstrates that the regions with the highest preparation of forestry specialists are located far from the Arctic zone and Northwest Russia. Meanwhile, the main forest resources of the macroregion's entities are unevenly distributed across their territories, concentrated mainly in the central and southern parts. The proposed mapping tool contributes to addressing the issues of personnel training for the forestry sector through the involvement of all interested actors: applicants, employers, secondary vocational education organizations, and regional executive authorities.

In standard statistical data analysis, the effects of intervention or prevention efforts are evaluated in terms of variable relations. Results from application of regression-type methods suggest whether, overall, intervention is successful. In this article, we propose using configural frequency analysis (CFA) either in tandem with regression-type methods or by itself. CFA allows one to adopt a person-oriented perspective in which individuals are targeted that can be characterized by particular profiles. The questions asked in CFA concern these individuals instead of variables. In prevention research, one can ask whether, for particular profiles, the preventive measures are successful. In three real-world data examples, CFA is applied and compared to standard log-linear modeling. Examples consider non-randomized (observational) and randomized intervention settings. The results of these analyses suggest that person-oriented CFA and standard variable-oriented methods of analysis respond to different questions. We show that integrating person- and variable-oriented perspectives can help researchers obtain a fuller picture of intervention effectiveness. Extensions of the CFA approach are discussed.

Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.

The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in logarithmic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log N)$</tex> time, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> stands for the number of observations and test points. Our approach uses the state-space representation of GPs which, in its original form, allows for linear <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N)$</tex> time GP regression by leveraging Kalman filtering and smoothing methods. By using a recently proposed parallelization method for Bayesian filters and smoothers, we are able to reduce the linear computational complexity of the temporal GP regression problems into logarithmic span complexity. This ensures logarithmic time complexity when parallel hardware such as a graphics processing unit (GPU) are employed. We experimentally show the computational benefits of our approach on simulated and real datasets via our open-source implementation leveraging the GPflow framework.

Crust and Lithospheric Structure - Active Source Studies of Crust and Lithospheric Structure