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Wildlife Study Design
Taking an approach from both a basic and applied perspective, the book covers numerous aspects of study design including variable classification, the necessity of randomization and replication in wildlife study design, the three major types of designs in decreasing order of rigor, detection probabilities, adaptive cluster methods, double sampling, sampling of rare species, effect size and power, and impact assessment, just to name a few. The concepts provided by the book make study design both accessible and comprehensive to a wide array of readers.
Understanding Statistics and Experimental Design : How to Not Lie with Statistics
Provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design.
Uncertainty, Rationality, and Agency
This book is about Rational Agents, which can be humans, players in a game, software programs or institutions. Typically, such agents are uncertain about the state of affairs or the state of other agents, and under this partial information they have to decide on which action to take next. This book collects chapters that give formal accounts not only of Uncertainty, Rationality and Agency, but also of their interaction: what are rational criteria to accept certain beliefs, or to modify them; how can degrees of beliefs guide an agent in making decisions; why distinguish between practical and epistemic rationality when agents try to coordinate; what must be common beliefs between agents about each other's rationality in order to act rationally themselves; can an agent assign probabilities to planned actions; how to formalise assumptions about a rational speaker in a conversation obeying Gricean maxims.
Uncertainty in Engineering : Introduction to Methods and Applications
Provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling.
The Physical Basis of The Direction of Time
This thoroughly revised 5th edition of Zeh's classic text investigates irreversible phenomena and their foundation in classical, quantum and cosmological settings. It includes new sections on the meaning of probabilities in a cosmological context, irreversible aspects of quantum computers, and various consequences of the expansion of the Universe.
The Descent of Human Sex Ratio at Birth : A Dialogue between Mathematics, Biology and Sociology
"Since the 18th century, one phenomenon, the proportion of the sexes at birth among human beings, has contributed to various developments such as the calculus of probabilities, administrative statistics, the moral and social sciences, the statistics of variability, post-Darwinian biology and Durkheimian sociology. This fact is brought to the critical attention of readers who rarely work together -- mathematicians, biologists, historians, social scientists and historians of the sciences -- along a three centuries European journey, meeting Süssmilch, Condorcet, Laplace, Fourier, Girou de Buzareingues, Poisson, Quetelet, Darwin, Düsing, Gini, Halbwachs or Fisher."
Stochastic Optimization Methods ; 1st ed.
Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.
Stochastic Optimization Methods ; 2nd ed.
Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations.
Stochastic Discrete Event Systems : Modeling, Evaluation, Applications
The behavior of many technical systems important in everyday life can be described using discrete states and state-changing events. Stochastic discrete-event systems (SDES) capture the randomness in choices and over time due to activity delays and the probabilities of decisions. The starting point for the evaluation of quantitative issues like performance and dependability is a formal description of the system of interest in a model. An abstract model class for SDES is presented as a pivotal unifying result. Several important model classes, including queuing networks, Petri nets and automata, are detailed together with their formal translation into this abstract model class. Standard and recently developed algorithms for the performance evaluation, optimization and control of SDES are presented in the context of the abstract model class. The necessary software tool support is also covered. The book is completed with nontrivial examples from areas like manufacturing control, performance of communication systems, and supply-chain management, highlighting the application of the techniques presented.
Simulating Fuzzy Systems
Simulating Fuzzy Systems demonstrates how many systems naturally become fuzzy systems and shows how regular (crisp) simulation can be used to estimate the alpha-cuts of the fuzzy numbers used to analyze the behavior of the fuzzy system. This monograph presents a concise introduction to fuzzy sets, fuzzy logic, fuzzy estimation, fuzzy probabilities, fuzzy systems theory and fuzzy computation. It also presents a wide selection of simulation applications ranging from emergency rooms to machine shops to project scheduling showing the varieties of fuzzy systems.
Risk assessment : Theory, methods, and applications
The book begins with an introduction of risk analysis, assessment, and management, and includes a new section on the history of risk analysis. It covers hazards and threats, how to measure and evaluate risk, and risk management. It also adds new sections on risk governance and risk-informed decision making; combining accident theories and criteria for evaluating data sources; and subjective probabilities. The risk assessment process is covered, as are how to establish context; planning and preparing; and identification, analysis, and evaluation of risk. Risk Assessment also offers new coverage of safe job analysis and semi-quantitative methods, and it discusses barrier management and HRA methods for offshore application. Finally, it looks at dynamic risk analysis, security and life-cycle use of risk.
Probability, statistics, and random processes for electrical engineering
While helping students to develop their problem-solving skills, the author motivates students with practical applications from various areas of ECE that demonstrate the relevance of probability theory to engineering practice.
Probabilistic Machine Learning for Civil Engineers
An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises
Pricing of Bond Options : Unspanned Stochastic Volatility and Random Field Models
A major theme of this book is the development of a consistent unified model framework for the evaluation of bond options. In general options on zero bonds (e.g. caps) and options on coupon bearing bonds (e.g. swaptions) are linked by no-arbitrage relations through the correlation structure of interest rates. Therefore, unspanned stochastic volatility (USV) as well as Random Field (RF) models are used to model the dynamics of entire yield curves. The USV models postulate a correlation between the bond price dynamics and the subordinated stochastic volatility process, whereas Random Field models allow for a deterministic correlation structure between bond prices of different terms. Then the pricing of bond options is done either by running a Fractional Fourier Transform or by applying the Integrated Edgeworth Expansion approach. The latter is a new extension of a generalized series expansion of the (log) characteristic function, especially adapted for the computation of exercise probabilities.
Premiers pas en statistique = First steps in statistics
This book introduces the fundamental concepts of statistical theory and describes the methods most often used in practice. It is intended for students of economics and social sciences whose study program includes a broad knowledge of statistical methods. It is also aimed at researchers in various fields of applied sciences as well as students who plan to pursue a more in-depth study of statistical theory and its applications at a later stage. The work has three parts: descriptive statistics, probabilities and inferential statistics.
Premiers pas en simulation = First steps in simulation
Why simulation techniques? Simulation methods, designed for use in statistics and operations research, have experienced and continue to develop rapidly due to the extraordinary evolution of computers. Applications are found in industry and in economics, or even social sciences, in particle physics, in astronomy and in many other fields. In many situations, whether in everyday life or in scientific research, the researcher is faced with problems which he seeks solutions on the basis of certain initial assumptions and constraints. To solve this type of problem, there exist analytical methods applicable to situations where the model makes it possible to treat the di? Erent variables by mathematically manageable equations, and numerical methods where the complexity of the model imposes a fragmentation of the problem, in particular by the identification of the various variables which come into play and the study of their interactions. This last approach is often accompanied by a large mass of calculations. Simulation techniques are numerical techniques: to simulate a phenomenon essentially means to carefully reconstruct its evolution.
Point Process Theory and Applications : Marked Point and Piecewise Deterministic Processes
Offers a mathematically rigorous exposition of the basic theory of marked point processes developing randomly over time, and shows how this theory may be used to treat piecewise deterministic stochastic processes in continuous time. The focus is on point processes that generate only finitely many points in finite time intervals, resulting in piecewise deterministic processes with "few jumps". The point processes are constructed from scratch with detailed proofs and their distributions characterized using compensating measures and martingale structures. Piecewise deterministic processes are defined and identified with certain marked point processes, which are then used in particular to construct and study a large class of piecewise deterministic Markov processes, whether time homogeneous or not. The second part of the book addresses applications of the just developed theory. This analysis of various models in applied statistics and probability includes examples and exercises in survival analysis, branching processes, ruin probabilities, sports (soccer), finance and risk management (arbitrage and portfolio trading strategies), and queueing theory.
Parameter Setting in Evolutionary Algorithms
One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
Noise-Induced Phenomena in Slow-Fast Dynamical Systems : A Sample-Paths Approach
Stochastic differential equations play an increasingly important role in modeling the dynamics of a large variety of systems in the natural sciences, and in technological applications. This book presents a new constructive approach to the quantitative description of solutions to systems of stochastic differential equations evolving on well-separated timescales. The method, which combines techniques from stochastic analysis and singular perturbation theory, allows the domains of concentration for typical sample paths to be determined, and provides precise estimates on the transition probabilities between these domains. In addition to the detailed presentation of the set-up and mathematical results, applications to problems in physics, biology, and climatology are discussed. The emphasis lies on noise-induced phenomena such as stochastic resonance, hysteresis, excitability, and the reduction of bifurcation delay.
Invariant Probabilities of Markov-Feller Operators and Their Supports
In this book invariant probabilities for a large class of discrete-time homogeneous Markov processes known as Feller processes are discussed. These Feller processes appear in the study of iterated function systems with probabilities, convolution operators, certain time series, etc. Rather than dealing with the processes, the transition probabilities and the operators associated with these processes are studied.
