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Nonparametric Monte Carlo Tests and Their Applications
A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability,empirical process theory.
New trends in computational vision and bio-inspired computing : Selected works presented at the ICCVBIC 2018, Coimbatore, India
Gathers selected, peer-reviewed original contributions presented at the International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC) conference which was held in Coimbatore, India, on November 29-30, 2018. The works included here offer a rich and diverse sampling of recent developments in the fields of Computational Vision, Fuzzy, Image Processing and Bio-inspired Computing. The topics covered include computer vision; cryptography and digital privacy; machine learning and artificial neural networks; genetic algorithms and computational intelligence; the Internet of Things; and biometric systems, to name but a few. The applications discussed range from security, healthcare and epidemic control to urban computing, agriculture and robotics.
New Algorithms for Macromolecular Simulation
Molecular simulation is a widely used tool in biology, chemistry, physics and engineering. This book contains a collection of articles by leading researchers who are developing new methods for molecular modelling and simulation. Topics addressed here include: multiscale formulations for biomolecular modelling, such as quantum-classical methods and advanced solvation techniques; protein folding methods and schemes for sampling complex landscapes; membrane simulations; free energy calculation; and techniques for improving ergodicity. The book is meant to be useful for practitioners in the simulation community and for those new to molecular simulation who require a broad introduction to the state of the art.
Multirate Statistical Signal Processing
This book introduces a statistical theory for extracting information from signals that have di?erent sampling rates. This new theory generalizes the conventional (deterministic) theory of multirate systems beyond many of its constraints.Furthermore,itallowsfortheformulationofseveralnewproblems such as spectrum estimation, time-delay estimation and sensor fusion in the realm of multirate signal processing. I have arrived at the theory presented here by integrating concepts from diverse areas such as information theory, inverse problems and theory of - equalities. The process of merging a variety of concepts of di?erent origin results in both merits and shortcomings. The former include the fresh and - di?erentiated view of an amateur, providing scope of application. The latter include a lack of in-depth experience in each of the original ?elds. Granted, this may lead to gaps in continuity, however it goes without saying that a complete theory can seldom be achieved by one person and in a short time.
Multiple Testing Procedures and Applications to Genomics
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. The methods are applied to a range of testing problems in biomedical and genomic research, including the identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments.
Motivation and career satisfaction at higher education in Syria: A sample from private university
This research paper examines the effect of motivational factors , extrinsic and intrinsic factors which effect on staff satisfaction at work at universities in Syria. The research methodology employs a quantitative design of questionnaire instrument. The model predicts that if employees develop high levels of motivation in their work and organizations, this will stimulate a good quality in their productivity and develop satisfaction at work. Motivation in general , extrinsic factors, intrinsic factors and job satisfaction are based on prior research measures. Sampling strategy employed non-probability sampling. The size of the sample is 35. The results of the research designate that intrinsic and extrinsic motivation factors are positively associated with employee job satisfaction
Mosquito Ecology : Field Sampling Methods
The purpose of this book is in keeping with the original vision of Professor Service to describe the methods and rationale for sampling mosquitoes, with particular emphasis on the ecology and behaviour of those species that play a role as vectors of human and animal diseases and infections. The book is designed to serve as a practical reference for field entomologists and mosquito control specialists and describes the sampling methods and available trapping technologies and tools for the collection of all life-stages of mosquitoes, from egg to adult. It also describes the techniques available for data analysis and discusses ecological principles of relevance to the study of field populations of mosquitoes. While concentrating primarily on mosquitoes, many of the techniques described are suitable for the study of other Diptera, including Ceratopogonidae, Chironomidae, Simuliidae, Phlebotominae, etc.
Modern Sliding Mode Control Theory : New Perspectives and Applications
This book is a collection of invited chapters covering several areas of modern sliding mode control theory. The authors identify key contributions defining the theoretical and applicative state of the art of the sliding mode control theory and the most promising trends of the ongoing research activities. The contributions is divided in four main parts: Part I: Basic Theory. Part II: Design Methods. Part III: Observers and Fault Detection. Part IV: Applications.
Modern Portfolio Optimization with NuOPT™, S-PLUS®, and S+Bayes™
This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book.
Modern Morphometrics in Physical Anthropology
Morphometrics has undergone a revolutionary transformation in the past two decades as new methods have been developed to address shortcomings in the traditional multivirate analysis of linear distances, angles, and indices. While there is much active research in the field, the new approaches to shape analysis are already making significant and ever-increasing contributions to biological research, including physical anthropology. Modern Morphometrics in Physical Anthropology highlights the basic machinery of the most important methods, while introducing novel extensions to these methods and illustrating how they provide enhanced results compared to more traditional approaches. Modern Morphometrics in Physical Anthropology provides a comprehensive sampling of the applications of modern, sophisticated methods of shape analysis in anthropology, and serves as a starting point for the exploration of these practices by students and researchers who might otherwise lack the local expertise or training to get started. This text is an important resource for the general morphometric community that includes ecologists, evolutionary biologists, systematists, and medical researchers.
MATLAB® Recipes for Earth Sciences
MATLAB® is used in a wide range of applications in geosciences, such as image processing in remote sensing, generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. The revised and updated Second Edition includes new subchapters on windowed Blackman-Tukey, Lomb-Scargle and Wavelet powerspectral analysis, statistical analysis of point distributions and digital elevation models, and a full new chapter on the statistical analysis of directional data. The text includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Introduction to Bayesian Statistics
This is the second and translated edition of the German book “Einf ̈uhrung in die Bayes-Statistik, Springer-Verlag, Berlin Heidelberg New York, 2000”. It has been completely revised and numerous new developments are pointed out together with the relevant literature. The Chapter 5.2.4 is extended by the stochastic trace estimation for variance components. The new Chapter 5.2.6 presents the estimation of the regularization parameter of type Tykhonov regularization for inverse problems as the ratio of two variance components.The reconstruction and the smoothing of digital three-dimensional images is demonstrated in the new Chapter 5.3. The Chapter 6.2.1 on importance sampling for the Monte Carlo integration is rewritten to solve a more general integral. This chapter contains also the derivation of the SIR (sampling-importance-resampling) algorithm as an alternative to the rejection method for generating random samples. Markov Chain Monte Carlo methods are now frequently applied in Bayesian statistics.
Instrumaster
Experiments with different neural network structures and algorithms in order to achieve musical note recognition as well as musical instrument recognition, all bundled in a mobile application. It also aims to create the most effective music-learning application that works completely offline, which is hard to find in modern music applications. The paper also explores why the instrument identifying AI is solely based on Multi-Layer Perceptron (MLP) and why the note-identifying AI system was chosen to be a ML system over CNN or other deep-learning trained AI. The paper presents feature extraction methods for audio signals and files and dives deep into the process, such as FFT, MFCCs, Wavelengths, sampling rates, etc. It also touches on Logistic Regression Algorithms, their limitations, and their performance with the different use cases in the application. All these techniques are then compared side by side for maximally added value, making this research paper a good reference for any future developers looking to find optimal neural networks techniques when it comes to audio processing and analysis.
Inference for change point and post change means after a CUSUM test
This monograph is the first to systematically study the bias of estimators and construction of corrected confidence intervals for change-point and post-change parameters after a change is detected by using a CUSUM procedure. Researchers in change-point problems and sequential analysis, time series and dynamic systems, and statistical quality control will find that the methods and techniques are mostly new and can be extended to more general dynamic models where the structural and distributional parameters are monitored. Practitioners, who are interested in applications to quality control, dynamic systems, financial markets, clinical trials and other areas, will benefit from case studies based on data sets from river flow, accident interval, stock prices, and global warming. Readers with an elementary probability and statistics background and some knowledge of CUSUM procedures will be able to understand most results as the material is relatively self-contained.The exponential family distribution is used as the basic model that includes changes in mean, variance, and hazard rate as special cases. There are fundamental differences between the sequential sampling plan and fixed sample size. Although the results are given under the CUSUM procedure, the methods and techniques discussed provide new approaches to deal with inference problems after sequential change-point detection, and they also contribute to the theoretical aspects of sequential analysis. Many results are of independent interests and can be used to study random walk related stochastic models.
Indirect Sampling
This book is the reference on indirect sampling and the generalised weight share method. It contains the different developments done by the author on these subjects. The theory surrounding them is presented, but also different possible applications that drive its interest. The reader will find in this book the answer to questions that come, inevitably, when working in a context of indirect sampling.
Image-Based Rendering
Image-based rendering (IBR) refers to a collection of techniques and representations that allows 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction. The potential for photorealistic visualization has tremendous appeal, especially for applications such as video games, virtual travel, and E-commerce, which stand to greatly benefit from this technology.
How Data Quality Affects our Understanding of the Earnings Distribution
This book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error.
Hierarchical Bayesian Optimization Algorithm : Toward a New Generation of Evolutionary Algorithms
This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope.
Hexagonal image processing : A practical approach
Hexagonal Image Processing provides an introduction to the processing of hexagonally sampled images, includes a survey of the work done in the field, and presents a novel framework for hexagonal image processing (HIP) based on hierarchical aggregates. Digital image processing is currently dominated by the use of square sampling lattices, however, hexagonal sampling lattices can also be used to define digital images. The strengths offered by hexagonal lattices over square lattices are considerable: • higher packing density, • uniform connectivity of points (pixels) in the lattice, • better angular resolution by virtue of having more nearest neighbours, and • superlative representation of curves. The utility of the HIP framework is demonstrated by implementing several basic image processing techniques (for the spatial and frequency domain) and some applications. The HIP framework serves as a tool for comparing processing of images defined on a square vs hexagonal grid, to determine their relative merits and demerits. The theory and algorithms covered are supplemented by attention to practical details such as accommodating hardware that support only images sampled on a square lattice. Including a Foreword written by Professor Narendra Ahuja, an eminent researcher in the field of Image Processing and Computer Vision, the book’s fresh approach to the subject offers insight and workable know-how to both researchers and postgraduates.
