About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 98. Chapters: Monte Carlo methods, Stochastic optimization, Box-Muller transform, Inverse transform sampling, Metropolis-Hastings algorithm, Simulated annealing, Metropolis light transport, Ant colony optimization, CMA-ES, Biology Monte Carlo method, Monte Carlo methods for electron transport, Monte Carlo methods in finance, Resampling, Randomized algorithm, Ensemble Kalman filter, Monte Carlo integration, Quasi-Monte Carlo methods in finance, Ensemble forecasting, Monte Carlo method for photon transport, Particle filter, Importance sampling, Stochastic computing, Kinetic Monte Carlo, Gillespie algorithm, Least mean squares filter, Multi-armed bandit, Monte Carlo methods for option pricing, Nicholas Metropolis, Markov chain Monte Carlo, Reverse Monte Carlo, Evolution strategy, Stochastic gradient descent, Cross-entropy method, Simultaneous perturbation stochastic approximation, Equation of State Calculations by Fast Computing Machines, Multiple-try Metropolis, Stochastic programming, Event generator, Scenario optimization, Auxiliary field Monte Carlo, BRST algorithm, Quantum annealing, Coupling from the past, Rejection sampling, Stochastic diffusion search, Estimation of distribution algorithm, Demon algorithm, Marsaglia polar method, Random search, Control variates, Umbrella sampling, Antithetic variates, Variance reduction, Direct simulation Monte Carlo, Stochastic tunneling, VEGAS algorithm, Reversible-jump Markov chain Monte Carlo, Monte Carlo molecular modeling, Low Energy Adaptive Clustering Hierarchy, Monte Carlo project, Dynamic Monte Carlo method, Auxiliary particle filter, Stochastic universal sampling, Wolff algorithm, Swendsen-Wang algorithm, Stochastic neural network. Excerpt: Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to compute thei...
