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