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This book discusses some aspects of the theory of partial differential equations from the viewpoint of probability theory. It is intended not only for specialists in partial differential equations or probability theory but also for specialists in asymptotic methods and in functional analysis. It is also of interest to physicists who use functional integrals in their research. The work contains results that have not previously appeared in book form, including research contributions of the author.

Partial differential equations --- Differential equations, Partial. --- Probabilities. --- Integration, Functional. --- Functional integration --- Functional analysis --- Integrals, Generalized --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- A priori estimate. --- Absolute continuity. --- Almost surely. --- Analytic continuation. --- Axiom. --- Big O notation. --- Boundary (topology). --- Boundary value problem. --- Bounded function. --- Calculation. --- Cauchy problem. --- Central limit theorem. --- Characteristic function (probability theory). --- Chebyshev's inequality. --- Coefficient. --- Comparison theorem. --- Continuous function (set theory). --- Continuous function. --- Convergence of random variables. --- Cylinder set. --- Degeneracy (mathematics). --- Derivative. --- Differential equation. --- Differential operator. --- Diffusion equation. --- Diffusion process. --- Dimension (vector space). --- Direct method in the calculus of variations. --- Dirichlet boundary condition. --- Dirichlet problem. --- Eigenfunction. --- Eigenvalues and eigenvectors. --- Elliptic operator. --- Elliptic partial differential equation. --- Equation. --- Existence theorem. --- Exponential function. --- Feynman–Kac formula. --- Fokker–Planck equation. --- Function space. --- Functional analysis. --- Fundamental solution. --- Gaussian measure. --- Girsanov theorem. --- Hessian matrix. --- Hölder condition. --- Independence (probability theory). --- Integral curve. --- Integral equation. --- Invariant measure. --- Iterated logarithm. --- Itô's lemma. --- Joint probability distribution. --- Laplace operator. --- Laplace's equation. --- Lebesgue measure. --- Limit (mathematics). --- Limit cycle. --- Limit point. --- Linear differential equation. --- Linear map. --- Lipschitz continuity. --- Markov chain. --- Markov process. --- Markov property. --- Maximum principle. --- Mean value theorem. --- Measure (mathematics). --- Modulus of continuity. --- Moment (mathematics). --- Monotonic function. --- Navier–Stokes equations. --- Nonlinear system. --- Ordinary differential equation. --- Parameter. --- Partial differential equation. --- Periodic function. --- Poisson kernel. --- Probabilistic method. --- Probability space. --- Probability theory. --- Probability. --- Random function. --- Regularization (mathematics). --- Schrödinger equation. --- Self-adjoint operator. --- Sign (mathematics). --- Simultaneous equations. --- Smoothness. --- State-space representation. --- Stochastic calculus. --- Stochastic differential equation. --- Stochastic. --- Support (mathematics). --- Theorem. --- Theory. --- Uniqueness theorem. --- Variable (mathematics). --- Weak convergence (Hilbert space). --- Wiener process.

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The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay--a phenomenon often referred to as long-memory or long-range dependence. An example of this is the absolute returns of equity data in finance. Selfsimilar stochastic processes (particularly fractional Brownian motion) have long been postulated as a means to model this behavior, and the concept of selfsimilarity for a stochastic process is now proving to be extraordinarily useful. Selfsimilarity translates into the equality in distribution between the process under a linear time change and the same process properly scaled in space, a simple scaling property that yields a remarkably rich theory with far-flung applications. After a short historical overview, this book describes the current state of knowledge about selfsimilar processes and their applications. Concepts, definitions and basic properties are emphasized, giving the reader a road map of the realm of selfsimilarity that allows for further exploration. Such topics as noncentral limit theory, long-range dependence, and operator selfsimilarity are covered alongside statistical estimation, simulation, sample path properties, and stochastic differential equations driven by selfsimilar processes. Numerous references point the reader to current applications. Though the text uses the mathematical language of the theory of stochastic processes, researchers and end-users from such diverse fields as mathematics, physics, biology, telecommunications, finance, econometrics, and environmental science will find it an ideal entry point for studying the already extensive theory and applications of selfsimilarity.

Self-similar processes. --- Distribution (Probability theory) --- Processus autosimilaires --- Distribution (Théorie des probabilités) --- 519.218 --- Self-similar processes --- 519.24 --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Selfsimilar processes --- Stochastic processes --- Special stochastic processes --- 519.218 Special stochastic processes --- Distribution (Théorie des probabilités) --- Almost surely. --- Approximation. --- Asymptotic analysis. --- Autocorrelation. --- Autoregressive conditional heteroskedasticity. --- Autoregressive–moving-average model. --- Availability. --- Benoit Mandelbrot. --- Brownian motion. --- Central limit theorem. --- Change of variables. --- Computational problem. --- Confidence interval. --- Correlogram. --- Covariance matrix. --- Data analysis. --- Data set. --- Determination. --- Fixed point (mathematics). --- Foreign exchange market. --- Fractional Brownian motion. --- Function (mathematics). --- Gaussian process. --- Heavy-tailed distribution. --- Heuristic method. --- High frequency. --- Inference. --- Infimum and supremum. --- Instance (computer science). --- Internet traffic. --- Joint probability distribution. --- Likelihood function. --- Limit (mathematics). --- Linear regression. --- Log–log plot. --- Marginal distribution. --- Mathematica. --- Mathematical finance. --- Mathematics. --- Methodology. --- Mixture model. --- Model selection. --- Normal distribution. --- Parametric model. --- Power law. --- Probability theory. --- Publication. --- Random variable. --- Regime. --- Renormalization. --- Result. --- Riemann sum. --- Self-similar process. --- Self-similarity. --- Simulation. --- Smoothness. --- Spectral density. --- Square root. --- Stable distribution. --- Stable process. --- Stationary process. --- Stationary sequence. --- Statistical inference. --- Statistical physics. --- Statistics. --- Stochastic calculus. --- Stochastic process. --- Technology. --- Telecommunication. --- Textbook. --- Theorem. --- Time series. --- Variance. --- Wavelet. --- Website.

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In this classic of statistical mathematical theory, Harald Cramér joins the two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probabilitists transformed the classical calculus of probability into a rigorous and pure mathematical theory. The result of Cramér's work is a masterly exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. For anyone with a working knowledge of undergraduate mathematics the book is self contained. The first part is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling, statistical estimation, and tests of significance.

Mathematical statistics --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistique mathématique --- Mathematical statistics. --- Statistique mathématique --- Statistique mathématique. --- Distribution (théorie des probabilités) --- Distribution (Probability theory) --- A priori probability. --- Addition theorem. --- Additive function. --- Analysis of covariance. --- Arithmetic mean. --- Axiom. --- Bayes' theorem. --- Bias of an estimator. --- Binomial distribution. --- Binomial theorem. --- Bolzano–Weierstrass theorem. --- Borel set. --- Bounded set (topological vector space). --- Calculation. --- Cartesian product. --- Central moment. --- Characteristic function (probability theory). --- Characteristic polynomial. --- Coefficient. --- Commutative property. --- Confidence interval. --- Convergence of random variables. --- Correlation coefficient. --- Degeneracy (mathematics). --- Degrees of freedom (statistics). --- Diagram (category theory). --- Dimension. --- Distribution (mathematics). --- Distribution function. --- Empirical distribution function. --- Equation. --- Estimation theory. --- Estimation. --- Identity matrix. --- Independence (probability theory). --- Interval (mathematics). --- Inverse probability. --- Invertible matrix. --- Joint probability distribution. --- Laplace distribution. --- Lebesgue integration. --- Lebesgue measure. --- Lebesgue–Stieltjes integration. --- Likelihood function. --- Limit (mathematics). --- Linear regression. --- Logarithm. --- Logarithmic derivative. --- Logarithmic scale. --- Marginal distribution. --- Mathematical analysis. --- Mathematical induction. --- Mathematical theory. --- Mathematics. --- Matrix (mathematics). --- Maxima and minima. --- Measure (mathematics). --- Method of moments (statistics). --- Metric space. --- Minor (linear algebra). --- Moment (mathematics). --- Moment matrix. --- Normal distribution. --- Numerical analysis. --- Parameter. --- Parity (mathematics). --- Poisson distribution. --- Probability distribution. --- Probability theory. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Realization (probability). --- Riemann integral. --- Sample space. --- Sampling (statistics). --- Scientific notation. --- Series (mathematics). --- Set (mathematics). --- Set function. --- Sign (mathematics). --- Standard deviation. --- Statistic. --- Statistical Science. --- Statistical hypothesis testing. --- Statistical inference. --- Statistical regularity. --- Statistical theory. --- Subset. --- Summation. --- Theorem. --- Theory. --- Transfinite number. --- Uniform distribution (discrete). --- Variable (mathematics). --- Variance. --- Weighted arithmetic mean. --- Z-test. --- Distribution (théorie des probabilités)

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Graduate students in the natural sciences-including not only geophysics and space physics but also atmospheric and planetary physics, ocean sciences, and astronomy-need a broad-based mathematical toolbox to facilitate their research. In addition, they need to survey a wider array of mathematical methods that, while outside their particular areas of expertise, are important in related ones. While it is unrealistic to expect them to develop an encyclopedic knowledge of all the methods that are out there, they need to know how and where to obtain reliable and effective insights into these broader areas. Here at last is a graduate textbook that provides these students with the mathematical skills they need to succeed in today's highly interdisciplinary research environment. This authoritative and accessible book covers everything from the elements of vector and tensor analysis to ordinary differential equations, special functions, and chaos and fractals. Other topics include integral transforms, complex analysis, and inverse theory; partial differential equations of mathematical geophysics; probability, statistics, and computational methods; and much more. Proven in the classroom, Mathematical Methods for Geophysics and Space Physics features numerous exercises throughout as well as suggestions for further reading. Provides an authoritative and accessible introduction to the subject Covers vector and tensor analysis, ordinary differential equations, integrals and approximations, Fourier transforms, diffusion and dispersion, sound waves and perturbation theory, randomness in data, and a host of other topics Features numerous exercises throughout Ideal for students and researchers alike an online illustration package is available to professors

Geophysics --- Cosmic physics --- Physics --- Space sciences --- Mathematics. --- Analytical mechanics. --- Applied mathematics. --- Atmospheric physics. --- Bessel function. --- Bifurcation theory. --- Calculation. --- Calculus of variations. --- Cartesian coordinate system. --- Cauchy's theorem (geometry). --- Celestial mechanics. --- Central limit theorem. --- Chaos theory. --- Classical electromagnetism. --- Classical mechanics. --- Classical physics. --- Convolution theorem. --- Deformation (mechanics). --- Degeneracy (mathematics). --- Diagram (category theory). --- Differential equation. --- Drag (physics). --- Earth science. --- Eigenvalues and eigenvectors. --- Einstein notation. --- Elliptic integral. --- Elliptic orbit. --- Equation. --- Expectation value (quantum mechanics). --- Figure of the Earth. --- Forcing function (differential equations). --- Fourier series. --- Fourier transform. --- Fractal dimension. --- Function (mathematics). --- Gaussian function. --- Geochemistry. --- Geochronology. --- Geodesics in general relativity. --- Geometry. --- Geophysics. --- Gravitational acceleration. --- Gravitational constant. --- Gravitational potential. --- Gravitational two-body problem. --- Hamiltonian mechanics. --- Handbook of mathematical functions. --- Harmonic oscillator. --- Helmholtz equation. --- Hilbert transform. --- Hyperbolic partial differential equation. --- Integral equation. --- Isotope geochemistry. --- Lagrangian (field theory). --- Laplace transform. --- Laplace's equation. --- Laws of thermodynamics. --- Limit (mathematics). --- Line (geometry). --- Lorenz system. --- Mathematical analysis. --- Mathematical geophysics. --- Mathematical physics. --- Newton's law of universal gravitation. --- Newton's laws of motion. --- Newton's method. --- Newtonian dynamics. --- Numerical analysis. --- Numerical integration. --- Operator (physics). --- Orbit. --- Orbital resonance. --- Parseval's theorem. --- Partial differential equation. --- Perturbation theory (quantum mechanics). --- Perturbation theory. --- Planetary body. --- Planetary science. --- Poisson's equation. --- Pole (complex analysis). --- Proportionality (mathematics). --- Quantum mechanics. --- Rotation (mathematics). --- Satellite geodesy. --- Scalar (physics). --- Scientific notation. --- Separatrix (mathematics). --- Sign (mathematics). --- Space physics. --- Statistical mechanics. --- Stokes' theorem. --- Three-dimensional space (mathematics). --- Transformation geometry. --- Trapezoidal rule. --- Truncation error (numerical integration). --- Two-dimensional space. --- Van der Pol oscillator. --- Variable (mathematics). --- Vector space. --- Wave equation.

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Singularities of solutions of differential equations forms the common theme of these papers taken from a seminar held at the Institute for Advanced Study in Princeton in 1977-1978. While some of the lectures were devoted to the analysis of singularities, others focused on applications in spectral theory. As an introduction to the subject, this volume treats current research in the field in such a way that it can be studied with profit by the non-specialist.

Partial differential equations --- Differential equations, Linear --- Differential equations, Partial --- Equations différentielles linéaires --- Equations aux dérivées partielles --- Numerical solutions --- Congresses --- Solutions numériques --- Congrès --- Théorie asymptotique --- 517.95 --- -Differential equations, Partial --- -Partial differential equations --- Linear differential equations --- Linear systems --- Insect societies. --- Insects --- Congresses. --- Ecology. --- 517.95 Partial differential equations --- -517.95 Partial differential equations --- Equations différentielles linéaires --- Equations aux dérivées partielles --- Solutions numériques --- Congrès --- Théorie asymptotique --- -Hexapoda --- Insecta --- Pterygota --- Arthropoda --- Entomology --- Behavior, Animal --- Ecology --- Insecta. --- Insect societies --- Sociétés d'insectes --- Insectes --- Ecologie --- Numerical solutions&delete& --- Insects, Social --- Social insects --- Animal societies --- Behavior --- Insects. Springtails --- Animal ethology and ecology. Sociobiology --- Behavior, Animal. --- Équations aux dérivées partielles --- Solutions numériques. --- A priori estimate. --- Adjoint equation. --- Analytic continuation. --- Analytic function. --- Analytic manifold. --- Asymptote. --- Asymptotic analysis. --- Asymptotic distribution. --- Asymptotic expansion. --- Asymptotic formula. --- Big O notation. --- Calculus on manifolds. --- Canonical transformation. --- Characteristic equation. --- Characteristic function (probability theory). --- Codimension. --- Cohomology. --- Commutator. --- Complex manifold. --- Complex number. --- Continuous function (set theory). --- Continuous function. --- Covariant derivative. --- Diffeomorphism. --- Differential equation. --- Differential operator. --- Dirichlet problem. --- Eigenfunction. --- Eigenvalues and eigenvectors. --- Elementary proof. --- Elliptic boundary value problem. --- Equation. --- Equivalence class. --- Equivalence relation. --- Error term. --- Existence theorem. --- Existential quantification. --- Exponential function. --- Fourier integral operator. --- Fourier inversion theorem. --- Fourier transform. --- Functional calculus. --- Fundamental solution. --- Hamiltonian vector field. --- Hardy space. --- Harmonic analysis. --- Hermann Weyl. --- Hermitian adjoint. --- Hilbert space. --- Holomorphic function. --- Homogeneous function. --- Hyperbolic partial differential equation. --- Hyperfunction. --- Hypersurface. --- Inclusion map. --- Inequality (mathematics). --- Integer lattice. --- Integral transform. --- Irreducible representation. --- Lagrangian (field theory). --- Laplace operator. --- Limit (mathematics). --- Linear map. --- Local diffeomorphism. --- Manifold. --- Mathematical optimization. --- Maximal torus. --- Monotonic function. --- Ordinary differential equation. --- Oscillatory integral. --- Partial differential equation. --- Partition of unity. --- Poisson bracket. --- Poisson summation formula. --- Polynomial. --- Projection (linear algebra). --- Projective variety. --- Pseudo-differential operator. --- Regularity theorem. --- Renormalization. --- Riemann surface. --- Riemannian manifold. --- Riesz representation theorem. --- Self-adjoint operator. --- Self-adjoint. --- Sign (mathematics). --- Special case. --- Spectral theorem. --- Spectral theory. --- Summation. --- Support (mathematics). --- Symplectic geometry. --- Symplectic manifold. --- Taylor series. --- Theorem. --- Toeplitz operator. --- Trace class. --- Trigonometric polynomial. --- Unit disk. --- Variable (mathematics). --- Equations aux derivees partielles lineaires --- Équations aux dérivées partielles --- Solutions numériques.