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E-raamat: Parametric Estimates by the Monte Carlo Method [De Gruyter e-raamatud]

  • Formaat: 196 pages
  • Ilmumisaeg: 28-Feb-1999
  • Kirjastus: VSP International Science Publishers
  • ISBN-13: 9783110941951
Teised raamatud teemal:
  • De Gruyter e-raamatud
  • Hind: 215,94 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 196 pages
  • Ilmumisaeg: 28-Feb-1999
  • Kirjastus: VSP International Science Publishers
  • ISBN-13: 9783110941951
Teised raamatud teemal:
01/07 This title is now available from Walter de Gruyter. Please see www.degruyter.com for more information.

This monograph is devoted to the further development of parametric weight Monte Carlo estimates for solving linear and nonlinear integral equations, radiation transfer equations, and boundary value problems, including problems with random parameters. The use of these estimates leads to the construction of new, effective Monte Carlo methods for calculating parametric multiple derivatives of solutions and for the main eigenvalues.

The book opens with an introduction on the theory of weight Monte Carlo methods. The following chapters contain new material on solving boundary value problems with complex parameters, mixed problems to parabolic equations, boundary value problems of the second and third kind, and some improved techniques related to vector and nonlinear Helmholtz equations. Special attention is given to the foundation and optimization of the global 'walk on grid' method for solving the Helmholtz difference equation. Additionally, new Monte Carlo methods for solving stochastic radiation transfer problems are presented, including the estimation of probabilistic moments of corresponding critical parameters.
Introduction: weight unbiased Monte Carlo estimates
1(41)
Integral equations, linear functionals
1(1)
Terminating Markov chains
2(1)
Standard weight estimates in the Monte Carlo method, biasedness
3(5)
Variances of the standard estimates
8(2)
The main approaches to variance reduction
10(6)
The use of recurrent representations
16(6)
Randomization
22(5)
Vector estimates related to the triangular system of integral equations
27(2)
Calculation of parametric derivatives and the main eigenvalues of integral operators
29(4)
Test integral equations and problems
33(3)
The extension of unbiasedness conditions
36(4)
Approximate confidence intervals
40(2)
Parametric estimates for solving problems of mathematical physics
42(53)
Introductory information
42(6)
Solving the Helmholtz equation with a complex parameter
48(8)
Solution of boundary value problems of the second and third kinds
56(7)
Solution of the Dirichlet problem for the vector and nonlinear Helmholtz equations
63(11)
Estimating the main eigenvalue of the Laplace operator
74(6)
Global algorithms of the Monte Carlo method for solving n-dimensional difference equations
80(15)
Parametric estimates for studying the radiation transfer in inhomogeneous media
95(53)
Introductory information
95(3)
Calculation of parametric derivatives and critical values of parameters
98(6)
Use of the averaged estimates by the Monte Carlo method for the study of the effects of medium stochasticity
104(11)
Modelling the homogeneous stochastic fields
105(1)
Partially averaged weight estimates
106(1)
Finiteness conditions for the variance of a partially averaged weight estimate
107(1)
Asymptotic estimation of the passage probability
108(4)
Test problem
112(2)
Additional remarks
114(1)
Critical parameters of the particle transport process with multiplication in a stochastic medium
115(17)
Averaging the constants and the solution of the transfer equation
116(3)
Use of the diffusion approximation
119(1)
Estimation by the Monte Carlo method
120(4)
Use of the simplest mathematical models
124(7)
Use of the second order parametric derivatives
131(1)
New approach to path estimates in the Monte Carlo method
132(6)
Monte Carlo estimates for derivatives of polarized radiation
138(10)
A. The improvement of random number generators by modulo 1 summation 148(18)
A.1 Estimates of the nonuniformity of distributions of the congruent sums of random quantities
148(10)
A.2 Congruent sums of grid random quantities
158(2)
A.3 Improvement in the random number generators by congruent summation
160(6)
B. On modelling chemical reactions by the Monte Carlo method 166(15)
B.1 Introduction
166(1)
B.2 General scheme of chemical reaction modelling by the Monte Carlo method
167(5)
B.3 Conditions of coexistence of steady states in chemical systems
172(4)
B.4 Calculation of quasi-potentials of dynamic systems
176(2)
B.5 Examples
178(3)
C. One unsolved minimax problem 181(3)
References 184