Stochastic Geometry and Its Applications
No detailed description available for "Stochastic Geometry and Its Applications".
Positive Operators and Fixed-Point Theorems with Applications
R for Health Technology Assessment
Primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses.
Portfolio Optimization
This comprehensive guide to the world of financial data modeling and portfolio design is a must-read for anyone looking to understand and apply portfolio optimization in a practical context. It bridges the gap between mathematical formulations and the design of practical numerical algorithms. It explores a range of methods, from basic time series models to cutting-edge financial graph estimation approaches. The portfolio formulations span from Markowitz's original 1952 mean-variance portfolio to more advanced formulations, including downside risk portfolios, drawdown portfolios, risk parity portfolios, robust portfolios, bootstrapped portfolios, index tracking, pairs trading, and deep-learning portfolios. Enriched with a remarkable collection of numerical experiments and more than 200 figures, this is a valuable resource for researchers and finance industry practitioners. With slides, R and Python code examples, and exercise solutions available online, it serves as a textbook for portfolio optimization and financial data modeling courses, at advanced undergraduate and graduate level.
Applied Multiple Regression/Correlation Analysis for Aviation Research
Applied Multiple Regression/Correlation Analysis for Aviation Research describes and illustrates multiple regression/correlation (MRC) analysis in an aviation context and is the ideal textbook for research-oriented graduate aviation programs that require knowledge of advanced statistical strategies for analyzing research data.
R Companion to Epidemiology
A companion book for Epidemiology study design and data analysis 3rd edition. Aims to equip with sufficient knowledge to use R for practising epidemiology. Reworks the examples in epidemiology study design and data analysis using R, presenting the code followed by an explanation and its result.
Unlocking Data with SPSS
Unlocking Data with SPSS: From Curiosity to Competence is a complete and practical guide designed to lead students, researchers, and professionals from the basics of SPSS to advanced statistical techniques. Beginning with an intuitive introduction to SPSS software, readers learn how to define variables, manage data, and conduct essential descriptive statistics and visualizations. As the book progresses, it builds a strong foundation in inferential statistics, including hypothesis testing, t-tests, ANOVA, regression analysis, and non-parametric methods. Advanced topics like factor analysis, reliability testing, and multivariate techniques are also thoroughly explained with step-by-step SPSS procedures. Special emphasis is placed on applying SPSS in academic research, reporting results according to APA standards, and exploring discipline-specific applications across psychology, business, and health sciences. Real-world case studies ensure practical understanding and reinforce the connection between theory and application. Whether for thesis writing, professional research, or general skill-building, this book empowers readers to unlock the full potential of their data using SPSS.
Machine Learning and Soft Computing
This two part-volume CCIS constitutes the refereed proceedings of 9th International Conference, ICMLSC 2025, in Tokyo, Japan in January 24-26, 2025. The 39 full papers and 13 short papers included in this book were carefully reviewed and selected from 121 submissions. They follow the topical sections as below: Part I: Multimodal Data Analysis and Model Optimization; Basic Theories of Machine Learning and Emerging Application Technologies; and Intelligent Recommendation System Design and Privacy Security. Part II: Deep Learning Models and High-performance Computing; Data-driven Complex System Modeling and Intelligent Optimization Algorithms; and Image Analysis and Processing Methods based on AI.
Intelligent Computers, Algorithms, and Applications
This book constitutes the proceedings of the 4th BenchCouncil International Symposium on Intelligent Computers, Algorithms, and Applications, IC 2024, held in Guangzhou, China, during December 4-6, 2024. The 16 full papers included in this book were carefully reviewed and selected from 31 submissions. They were organized in topical sections as follows: Algorithms; Education; Evaluation; System.
Business Analytics
This book covers key topics related to business analytics like descriptive, predictive, and prescriptive analytics, data exploration, visualization, and preparation. There is emphasis on practical application, including advanced Excel features like pivot tables and lookup functions, alongside Python's capabilities for data preparation, regression analysis, and predictive modeling. Each chapter integrates examples, coding exercises, and step-by-step instructions. The primary purpose of this book is to equip readers with the knowledge and skills required to decode data using Advanced Excel and Python. It will not only simplify their business decision-making process but also enable them to predict future trends. It serves as a practical guide for students, educators, and professionals. Readers will learn about the three pillars of analytics: descriptive, predictive, and prescriptive, and understand why they're indispensable in organisational growth trajectories.
Analytical Study of Air Traffic Using ARFIMA Time Series Models
While time series forecasting techniques have been widely developed, the self-similar structure of data has not been adequately addressed. This research focuses on investigating self-similar structures in real-time air traffic data from Air India and Indigo's scheduled domestic flights, aiming to develop a suitable forecasting model for self-similar time series. Self-similarity has proven valuable, particularly in processes like ARFIMA, long-range dependence, and the Hurst parameter. This study explores the current understanding of self-similarity, its concepts, definitions, and applications, offering a roadmap for future research. The book consolidates past works on air traffic modeling using methods such as Box-Jenkins, Exponential Smoothing, and Artificial Neural Networks. It aims to present a comprehensive overview of time series forecasting developments, focusing on air traffic modeling, long-range dependence through self-similarity, and fitting ARFIMA to identify the most effective forecasting model.
The Silent Struggle
This book examines the systematic marginalisation of Hindus in West Bengal, tracing its roots from pre-Partition Bengal to modern-day socio-political dynamics. It explores political appeasement, Islamic radicalism, demographic shifts, and cultural erosion, supported by historical analysis and real-life case studies.The book concludes with a call to action for Hindus to reclaim their rights, heritage, and future.
Application of Bayesian Model Averaging to Carbon Emissions in Nigeria
In this book, the application of Bayesian Model Averaging in Environmental Statistics, were discussed. Uncertainties of variables in the model and elicitation of parameters were also extensively looked into. Series of literature were reviewed, especially those focusing on parameter prior elicitation and sensitivity in Bayesian Model Averaging as applicable to various milieus of science. To this end, three modified g-parameter priors were elicited and their asymptotic properties were derived in Bayesian Model Averaging. Specifically the modified g-parameter priors elicited are consistent in both the Posterior Model Probabilities and Bayes factor for the model. In addition, the sensitivities of these modified g-parameter priors were investigated using both the simulated results and data sets from carbon emissions in Nigeria from 1975 - 2015.
New Method for Time Series Forecast-Application of Rainfall Prediction
An overview of the conventional time series forecasting methods, including AR, MA, ARMA, ARIMA, SARIMA, ARCH, and GARCH models, is provided in this book. The trend pattern of rainfall in India is described in detail in Chapter 1, the traditional forecast models are described in Chapter 2, and the innovative way of building a time series model is discussed in Chapter 3. The validity of this method is tested using an appropriate data set of Indian rainfall.
Scalable Monte Carlo for Bayesian Learning
A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.
Magic, Mathematics, and Playing Cards
Drawing from their collective experience as math enthusiasts, the authors, who are co-founders of Mathematical Circus, have compiled a collection of mathematical activities centered around a standard deck of cards. This book presents a range of self-working card tricks, each rooted in mathematical principles, explained in a clear and straightforward manner. Designed to be both educational and entertaining, the book makes these mathematical concepts accessible to readers of all backgrounds.
Magic, Mathematics, and Playing Cards
Drawing from their collective experience as math enthusiasts, the authors, who are co-founders of Mathematical Circus, have compiled a collection of mathematical activities centered around a standard deck of cards. This book presents a range of self-working card tricks, each rooted in mathematical principles, explained in a clear and straightforward manner. Designed to be both educational and entertaining, the book makes these mathematical concepts accessible to readers of all backgrounds.
Partially Observed Markov Decision Processes
Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.
High-Accuracy Finite Difference Methods
Scientific computing plays a critically important role in almost all areas of engineering, modeling, and forecasting. The method of finite differences (FD) is a classical tool that is still rapidly evolving, with several key developments barely yet in the literature. Other key aspects of the method, in particular those to do with computations that require high accuracy, often 'fall through the cracks' in many treatises. Bengt Fornberg addresses that failing in this book, which adopts a practical perspective right across the field and is aimed at graduate students, scientists, and educators seeking a follow-up to more typical curriculum-oriented textbooks. The coverage extends from generating FD formulas and applying them to solving ordinary and partial differential equations, to numerical integration, evaluation of infinite sums, approximation of fractional derivatives, and computations in the complex plane.
R for Non-Programmers
The book introduces interactive elements, including chapter exercises in the accompanying R package, facilitating readers internalising this new programming language and statistical techniques. This interactive approach, particularly beneficial for novices, enhances the overall learning experience and distinguishes it as a valuable resource.
Copula Additive Distributional Regression Using R
Copula additive distributional regression enables the joint modeling of multiple outcomes, an essential aspect of many real-world research problems. This book provides an accessible overview of this modeling approach, with a particular focus on its implementation in the GJRM R package
Bioinspired Algorithms and Applications in Image Fusion
The preying strategies in animals and other living creatures have motivational intelligence which can be implemented significantly in computer algorithms designed for image fusion which is an eminent image processing technique. The research contributed in this book is the designing of few methodologies for the pixel type fusion of two magnetic resonance images harnessing the traits of different genres of discrete wavelet transform and Bayesian model. The parameters of this estimation model are then optimized employing distinctive nature encouraged optimization algorithms, for instance bird swarm algorithm (BSA), etc. to have an optimized fused image with peerless performance.
BustaBit
BustaBit: History, Strategies, and Mathematics of the Original Crash Game By Lennart Lopin Unlock the Secrets Behind the Game That Changed Crypto Gambling Forever. In BustaBit, Lennart Lopin pulls back the curtain on the first and most iconic Bitcoin crash game - the phenomenon that fused cryptocurrency, game theory, and human psychology into one electrifying experience. From its wild beginnings in Bitcoin's early days to its survival through a decade of volatility, Bustabit is a living experiment in risk, randomness, and raw decision-making. This book is the ultimate Bustabit bible: - The origin story of how crash gambling was born - The hidden mathematics driving every multiplier - Winning strategies and losing mindsets exposed - The psychology of gamblers, traders, and adrenaline-seekers - The rise of automation, bots, and the API underground - Hard lessons from real legends who won - and lost - fortunes If you love Bitcoin, gaming, statistics, trading, behavioral economics, or just crave a brutally honest exploration of what happens when money meets math meets human nature, this is the book you can't afford to miss. Learn what the house doesn't tell you. Play smarter. Understand deeper. Whether you're a seasoned crypto gambler, a curious trader, or a student of human behavior under pressure - BustaBit will change how you see risk, forever. Ready to outthink the crash? Start your journey today.
Mathematics
This book tells the stories of some of the great quests of mathematics, such as the centuries-long pursuit for the proof of Fermat's Last Theorem. These quests are searches for difficult-to-discover universal truths, pursued with passion not only by mathematicians and scientists, but by kings, emperors and even Jean-Luc Picard, the captain of Star Trek's Starship Enterprise. Some of their exploits are adventures as fascinating as any historical or current-day drama. The truths they have discovered help us understand not only mathematics, but also the Universe - and sometimes, ourselves.In addition to well-known quests such as Fermat's Last Theorem and the Goldbach Conjecture, some of the chapters describe more recent pursuits such as the Traveling Salesman Problem and the Multi-armed Bandit Problem. While some of the quests have been completed, others are still ongoing, and one (the Six Squares Problem) can be understood - and maybe even solved - by a five-year-old child.
Mathematics
This book tells the stories of some of the great quests of mathematics, such as the centuries-long pursuit for the proof of Fermat's Last Theorem. These quests are searches for difficult-to-discover universal truths, pursued with passion not only by mathematicians and scientists, but by kings, emperors and even Jean-Luc Picard, the captain of Star Trek's Starship Enterprise. Some of their exploits are adventures as fascinating as any historical or current-day drama. The truths they have discovered help us understand not only mathematics, but also the Universe - and sometimes, ourselves.In addition to well-known quests such as Fermat's Last Theorem and the Goldbach Conjecture, some of the chapters describe more recent pursuits such as the Traveling Salesman Problem and the Multi-armed Bandit Problem. While some of the quests have been completed, others are still ongoing, and one (the Six Squares Problem) can be understood - and maybe even solved - by a five-year-old child.
Time Series
Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computational tools. Methodology that was originally developed for specialized applications, for example in finance or geophysics, is now widely available within general statistical packages. The second edition of Time Series: A Biostatistical Introduction is an introductory account of time series analysis, written from the perspective of applied statisticians whose interests lie primarily in the biomedical and health sciences. This edition has a stronger focus on substantive applications, in which each statistical analysis is directed at a specific research question. Separate chapters cover simple descriptive methods of analysis, including time-plots, smoothing, the correlogram and the periodogram; theory of stationary random processes; discrete-time models for single series; continuous-time models for single series; generalized linear models for time series of counts; models for replicated series; spectral analysis, and bivariate time series. The book is unique in its focus on biomedical and health science applications, which has been strengthened in this second edition. Nevertheless, the methods described are more widely applicable. It should be useful to teachers and students on masters-level degree courses in statistics, biostatistics and epidemiology, and to biomedical and health scientists with a knowledge of statistical methods at undergraduate level. Throughout, examples based on real datasets show a close interplay between statistical method and substantive science. This book will also describe the implementation of the methods in the R computing environment and provide access to R code and datasets.
Decision Making Optimization Models for Business Partnerships
Efficiency and productivity improvement are imperative for businesses to remain competitive in an increasingly dynamic marketplace. While business organizations have the potential to thrive independently, collaborating with others fosters a collective strength that can lead to greater innovation, expanded reach, and shared success.Decision making optimization models for business partnerships are essential, as businesses seldom have all the resources they need, and thus, they require alliances and partnerships with others to enable them to meet their goals. Decision Making Optimization Models for Business Partnerships extends non-parametric data envelopment analysis (DEA) and parametric econometrics approaches to better understand how economic efficiency and market competitiveness are achieved for different types of partnerships and strategic alliances.Features Global contributions for a wide range of professionals and academics Invaluable resources for businesses, analysts, and academics interested in DEA, optimization, and operations research more widely Introduces readers to novel approaches, models, and decision making techniques on performance evaluation and business partnerships via the medium of parametric and nonparametric optimization.
Generalized Quantum Calculus with Applications
Generalized Quantum Calculus with Applications is devoted to the qualitative theory of general quantum calculus and its applications to general quantum differential equations and inequalities. The book is aimed at upper-level undergraduate students and beginning graduate students in a range of interdisciplinary courses including physical sciences and engineering, from quantum mechanics to differential equations, with pedagogically organized chapters that each concludes with a section of practical problems. Generalized quantum calculus includes a generalization of the q-quantum calculus and the time scale calculus. There are many open problems and difficulties in q-quantum calculus and time-scale calculus, and this book explores how to use the generalized quantum operators to solve difficulties arising in q-quantum calculus and time-scale calculus, including but not limited to generalized quantum integration, generalized quantum chain rules, and generalized quantum Taylor formula. Since generalized quantum calculus includes the q-quantum and time-scale calculus, this book can be utilized by a wide audience of researchers and students. This text is one of few foundational books on generalized quantum calculus and can be used for future discoveries in the area of integral transforms, variational calculus, integral equations, and inequalities in the language of generalized quantum calculus. This book also offers detailed proofs, exercises, and examples to aid instructors, researchers, and users in their studies.
Time Series: A Biostatistical Introduction
Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computational tools. Methodology that was originally developed for specialized applications, for example in finance or geophysics, is now widely available within general statistical packages. The second edition of Time Series: A Biostatistical Introduction is an introductory account of time series analysis, written from the perspective of applied statisticians whose interests lie primarily in the biomedical and health sciences. This edition has a stronger focus on substantive applications, in which each statistical analysis is directed at a specific research question. Separate chapters cover simple descriptive methods of analysis, including time-plots, smoothing, the correlogram and the periodogram; theory of stationary random processes; discrete-time models for single series; continuous-time models for single series; generalized linear models for time series of counts; models for replicated series; spectral analysis, and bivariate time series. The book is unique in its focus on biomedical and health science applications, which has been strengthened in this second edition. Nevertheless, the methods described are more widely applicable. It should be useful to teachers and students on masters-level degree courses in statistics, biostatistics and epidemiology, and to biomedical and health scientists with a knowledge of statistical methods at undergraduate level. Throughout, examples based on real datasets show a close interplay between statistical method and substantive science. This book will also describe the implementation of the methods in the R computing environment and provide access to R code and datasets.
Geocomputation with R
Geocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing.
A Course in Game Theory
Game theory is a fascinating subject. We all know many entertaining games, such as chess, poker, tic-tac-toe, bridge, baseball, computer games - the list is quite varied and almost endless. In addition, there is a vast area of economic games, discussed in Myerson (1991) and Kreps (1990), and the related political games [Ordeshook (1986), Shubik (1982), and Taylor (1995)]. The competition between firms, the conflict between management and labor, the fight to get bills through congress, the power of the judiciary, war and peace negotiations between countries, and so on, all provide examples of games in action. There are also psychological games played on a personal level, where the weapons are words, and the payoffs are good or bad feelings [Berne (1964)]. There are biological games, the competition between species, where natural selection can be modeled as a game played between genes [Smith (1982)]. There is a connection between game theory and the mathematical areas of logic and computer science. One may view theoretical statistics as a two-person game in which nature takes the role of one of the players, as in Blackwell and Girshick (1954) and Ferguson (1968).Games are characterized by a number of players or decision makers who interact, possibly threaten each other and form coalitions, take actions under uncertain conditions, and finally receive some benefit or reward or possibly some punishment or monetary loss. In this text, we present various mathematical models of games and study the phenomena that arise. In some cases, we will be able to suggest what courses of action should be taken by the players. In others, we hope simply to be able to understand what is happening in order to make better predictions about the future.
Applied Statistical Methods
This book is designed to provide students, teachers, and researchers with a text that includes a full range of statistical methods available to address commonly encountered research problems.
Fractional Order PID and ADR Controls
This book explores the design and analysis of fractional-order and active disturbance rejection control, examining both the theoretical foundations and their practical applications.It covers fractional-order proportional-integral-derivative (PID) control, fractional-order active disturbance rejection (ADR) control, and the combined fractional-order PID-ADR control. The book begins with an analysis of the three-parameter fractional-order PID controller, demonstrating its application to the permanent magnet synchronous motor (PMSM) speed servo system, due to its comprehensive inclusion of proportional, integral, and differential elements. It then delves into active disturbance rejection control and periodic disturbance compensation, comparing the performance of each controller based on various parameters. This comparison enables readers to critically evaluate the advantages and limitations of each approach before implementation. Offering a thorough guide to fractional-order and active disturbance rejection control, the book also includes numerical methods for assessing and developing these systems.The book will be of particular interest to professionals working with numerical methods, fractional-order systems and control, PID controller, active disturbance rejection, control design, and production and is especially relevant to those in mechanical, industrial, and electrical engineering.
Beyond Signals - Exploring Revolutionary Fourier Transform Applications
Fourier Transform is a fundamental mathematical framework that has revolutionized numerous scientific and technological domains. Beyond Signals - Exploring Revolutionary Fourier Transform Applications presents an in-depth analysis of its profound influence on modern research and industry. This volume explores advanced applications in signal processing, spectroscopy, quantum mechanics, biomedical imaging, nanomaterials, and renewable energy, illustrating how Fourier techniques enable precise data interpretation and system optimization. The book integrates theoretical foundations with practical implementations, offering insights into its role in material characterization, sensor technology, and computational modeling. Authored by distinguished experts, including Dr. Muhammad Bilal Tahir-recognized for his contributions to nanomaterials, optoelectronics, and applied physics-this work is a comprehensive resource for researchers, engineers, and scholars. By bridging classical theories with emerging advancements, Beyond Signals - Exploring Revolutionary Fourier Transform Applications highlights the transformative potential of Fourier Transform methodologies in solving complex scientific and engineering challenges.
Solution of Initial-Boundary Value Problems
Methods for solving problems of mathematical physics can be divided into the following four classes. Analytical methods (the method of separation of variables, the method of characteristics, the method of Green's functions, etc.) methods have a relatively low degree of universality, i.e. focused on solving rather narrow classes of problems. Approximate analytical methods (projection, variational methods, small parameter method, operational methods, various iterative methods) are more versatile than analytical ones. Numerical methods (finite difference method, direct method, control volume method, finite element method, etc.) are very universal methods. Probabilistic methods (Monte Carlo methods) are highly versatile. Can be used to calculate discontinuous solutions. However, they require large amounts of calculations and, as a rule, they lose with the computational complexity of the above methods when solving such problems to which these methods are applicable. Comparing methods for solving problems of mathematical physics, it is impossible to give unconditional primacy to any of them. Any of them may be the best for solving problems of a certain class. The proposed method of moving nodes for boundary value problems of differential equations combines a combination of numerical and analytical methods. In this case, we can obtain, on the one hand, an approximately analytical solution of the problem, which is not related to the methods listed above. On the other hand, this method allows one to obtain compact discrete approximations of the original problem. Note that obtaining an approximately analytical solution of differential equations is based on numerical methods. The nature of numerical methods also allows obtaining an approximate analytical expression for solving differential equations
Variance-Constrained Filtering for Stochastic Complex Systems
Connected Sets in Global Bifurcation Theory
This book explores the topological properties of connected and path-connected solution sets for nonlinear equations in Banach spaces, focusing on the distinction between these concepts. Building on Rabinowitz's dichotomy and classical results on Peano continua, the authors introduce "congestion points"--where connected sets fail to be weakly locally connected--and examine the extent to which their presence is compatible with path-connectedness. Through rigorous analysis and examples, the book provides new insights into global bifurcations. Structured into seven chapters, the book begins with an introduction to global bifurcation theory and foundational concepts in set theory and metric spaces. Subsequent chapters delve into connectedness, local connectedness, and congestion points, culminating in the construction of intricate examples that highlight the complexities of solution sets. The authors' careful selection of material and fluent writing style make this work a valuable resource for PhD students and experts in functional analysis and bifurcation theory.
Game Theory for Applied Econometricians
Over the last 30 years the practice and use of game theory has changed dramatically, yet textbooks continue to present game theory with algebraic formalism and toy models. This book, on the other hand, illustrates game theory concepts using real-world data and analyses problems with real policy implications.
Unique Continuation Properties for Partial Differential Equations
This book provides a comprehensive and self-contained introduction to the study of the Cauchy problem and unique continuation properties for partial differential equations. Aimed at graduate and advanced undergraduate students, it bridges foundational concepts such as Lebesgue measure theory, functional analysis, and partial differential equations with advanced topics like stability estimates in inverse problems and quantitative unique continuation. By presenting detailed proofs and illustrative examples, the text equips readers with a deeper understanding of these fundamental topics and their applications in mathematical analysis. Designed to serve as both a learning resource and a reference, this book is particularly suited for those pursuing research in mathematical physics, inverse problems, or applied analysis.
Deep Learning in Personalized Music Emotion Recognition
Music has a unique power to evoke strong emotions in us--bringing us to tears, lifting us into ecstasy or triggering vivid memories. Often described as a universal language, it conveys feelings that transcend words. But are machines, too, able to understand this language and capture emotions conveyed in music? This book delves into the field of Musical Emotion Recognition (MER), aiming to develop a mathematical model to predict the emotional content of music. It explores the fundamentals of this interdisciplinary research area, including the relationship between music and emotions, mathematical representations of music and deep learning algorithms. Two MER models are developed and evaluated: one employing handcrafted audio features with a long short-term memory architecture and the other using embeddings from the pre-trained music understanding model MERT. Results show that MERT embeddings can enhance predictions compared to traditional handcrafted features. Additionally, driven by the subjectivity of musical emotions and the low inter-rater agreement of annotations, this book investigates personalized emotion recognition. The findings suggest that personalized models surpass the limitations of general MER systems and can even outperform a theoretically perfect general MER system.
Complex Analysis and Dynamics in One Variable with Applications
