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Journal of the ACM, 1956
Let α be a random vector distributed over a space U with probability density function f(α). If U is an awkward space or f is a complicated function, it may be hard to estimate $$\theta = E\phi \left( \alpha \right).$$ (6.1.1) .
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Let α be a random vector distributed over a space U with probability density function f(α). If U is an awkward space or f is a complicated function, it may be hard to estimate $$\theta = E\phi \left( \alpha \right).$$ (6.1.1) .
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1987
The term ‘Monte Carlo methods’ is used to refer to two different, though closely related, techniques. The first meaning, currently the less common one among economists, is the evaluation of definite integrals by use of random variables. The idea is to evaluate \(\int_a^b {F\left( x \right)} {\text{d}}x\) where x may be a vector) by estimating \(\int_a ...
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The term ‘Monte Carlo methods’ is used to refer to two different, though closely related, techniques. The first meaning, currently the less common one among economists, is the evaluation of definite integrals by use of random variables. The idea is to evaluate \(\int_a^b {F\left( x \right)} {\text{d}}x\) where x may be a vector) by estimating \(\int_a ...
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GEM - International Journal on Geomathematics, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Monte Carlo / Monte Carlo Markov Chain
2014The Monte Carlo simulation is a versatile method for analyzing the behavior of some activities, plans or processes that involve uncertainty. The method was invented by scientists working on the atomic bomb in the 1940s. It uses randomness to obtain random variable estimates, similarly to the gambling process.
Castellano R., CEDROLA, ELENA
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2003
We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method.
Rasmussen, C., Ghahramani, Z.
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We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method.
Rasmussen, C., Ghahramani, Z.
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Quantum Monte Carlo and Related Approaches
Chemical Reviews, 2012William A Lester, Dmitry Yu Zubarev
exaly
Monte Carlo Estimation of Bayesian Credible and HPD Intervals
Journal of Computational and Graphical Statistics, 1999Ming-Hui Chen, Qi-Man Shao
exaly
Monte Carlo methods in geophysical inverse problems
Reviews of Geophysics, 2002Klaus Mosegaard, Malcolm Sambridge
exaly