multi dimensional monte carlo integrations von chowdhury sujaul (8 Ergebnisse)

Zustand: New. In.

Zustand: New.

Zustand: New.

Paperback. Zustand: new. Paperback. We have chosen suitable multi-variable probability density functions and obtained corresponding random variates using the random sampling method called acceptance-rejection method. This book provides hands-on and practical demonstrations of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated 2, 3, 5, 7 and 10 dimensional definite integrals. We performed symbolic computations using programs written in Mathematica(R), in which we performed the variate sampling and evaluated the integrals. The book is self-contained. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. This book provides practical, hands-on demonstration of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated a large number of multi-dimensional definite integrals using Monte Carlo method. These are 2, 3, 5, 7 and 10 dimensional definite integrals. We have used mean value method for the evaluations. We have performed function evaluations at random values of the variables gathered as uniform random variates using inverse transform sampling method. We have performed symbolic computations using programs written in Mathematica. Very much smaller number of function evaluations is found to lead to much lower error compared to the case with uniform, non-random sampling i.e. without using Monte Carlo method. This is consistent with what we often hear about a virtue of Monte Carlo method in evaluating multi-dimensional or high-dimensional definite integrals. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. We have chosen suitable multi-variable probability density functions and obtained corresponding random variates using the random sampling method called acceptance-rejection method. This book provides hands-on and practical demonstrations of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated 2, 3, 5, 7 and 10 dimensional definite integrals. We performed symbolic computations using programs written in Mathematica(R), in which we performed the variate sampling and evaluated the integrals. The book is self-contained. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. This book provides practical, hands-on demonstration of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated a large number of multi-dimensional definite integrals using Monte Carlo method. These are 2, 3, 5, 7 and 10 dimensional definite integrals. We have used mean value method for the evaluations. We have performed function evaluations at random values of the variables gathered as uniform random variates using inverse transform sampling method. We have performed symbolic computations using programs written in Mathematica. Very much smaller number of function evaluations is found to lead to much lower error compared to the case with uniform, non-random sampling i.e. without using Monte Carlo method. This is consistent with what we often hear about a virtue of Monte Carlo method in evaluating multi-dimensional or high-dimensional definite integrals. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. This book provides practical, hands-on demonstration of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated a large number of multi-dimensional definite integrals using Monte Carlo method. These are 2, 3, 5, 7 and 10 dimensional definite integrals. We have used mean value method for the evaluations. We have performed function evaluations at random values of the variables gathered as uniform random variates using inverse transform sampling method. We have performed symbolic computations using programs written in Mathematica. Very much smaller number of function evaluations is found to lead to much lower error compared to the case with uniform, non-random sampling i.e. without using Monte Carlo method. This is consistent with what we often hear about a virtue of Monte Carlo method in evaluating multi-dimensional or high-dimensional definite integrals. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.