Monte Carlo method
Label
Monte Carlo method
Name
Monte Carlo method
Focus
Actions
Incoming Resources
- Subject of30
- Nonparametric Monte Carlo tests and their applications, Lixing Zhu
- Monte Carlo, concepts, algorithms, and applications, George S. Fishman
- Risk analysis, a quantitative guide, David Vose
- Monte Carlo simulation and finance, Don L. McLeish
- Monte Carlo simulation, Christopher Z. Mooney
- Numerical methods for stochastic processes, by Nicolas Bouleau, Dominique Lépingle
- Monte Carlo methods and models in finance and insurance, Ralf Korn, Elke Korn, Gerald Kroisandt
- Interacting multiagent systems, kinetic equations and Monte Carlo methods, Lorenzo Pareschi (Department of Mathematics and Computer Science, University of Ferrara, Italy), Giuseppe Toscani (Department of Mathematics, University of Pavia, Italy)
- Simulation and Monte Carlo, with applications in finance and MCMC, J.S. Dagpunar
- Quantitative risk analysis, a guide to Monte Carlo simulation modelling, David Vose
- Bayesian models for categorical data, Peter Congdon
- Monte Carlo simulation, Christopher Z. Mooney
- Computational methods in statistics and econometrics, Hisashi Tanizaki
- Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
- Méthodes de Monte Carlo par chaînes de Markov, Christian Robert
- Markov chain Monte Carlo in practice, edited by W.R. Gilks, S. Richardson, and D.J. Spiegelhalter
- Monte Carlo statistical methods, Christian P. Robert, George Casella
- Simulation and the monte carlo method
- Mean field simulation for Monte Carlo integration, Pierre Del Moral
- Monte Carlo methods in financial engineering, Paul Glasserman
- Sequential Monte Carlo methods in practice, Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
- Monte Carlo simulation and resampling, methods for social science, Thomas M. Carsey, Jeffrey J. Harden
- Statistical simulation, power method polynomials and other transformations, Todd C. Headrick
- Monte carlo and quasi-monte carlo sampling, Christiane Lemieux
- Principles and methods for data science, edited by Arni S. R. Srinivasa Rao, C. R. Rao
- Markov-Switching Vector autoregressive models, Monte Carlo experiment, impulse response analysis, and Granger-Causal analysis, Matthieu Droumaguet
- Introducing Monte Carlo methods with R, Christian P. Robert, George Casella
- Nonstationarity and structural breaks in economic time series, asymptotic theory and Monte Carlo simulations, Antonio E. Noriega-Muro
- Randomization, bootstrap, and Monte Carlo methods in biology, Bryan F.J. Manly
- Bayesian full information analysis of simultaneous equation models using integration by Monte Carlo, Luc Bauwens
Outgoing Resources
- Focus1