Books on the topic 'Montel theorem'

Joseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0.

Fox, Bennett L. Strategies for Quasi-Monte Carlo. Boston, MA: Springer US, 1999.

American Chemical Society. Division of Physical Chemistry, ed. Advances in quantum Monte Carlo. Washington, DC: American Chemical Society, 2012.

Fu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.

László, Koblinger, ed. Monte Carlo particle transport methods: Neutron and photon calculations. Boca Raton: CRC Press, 1991.

Monte Carlo optimization, simulation, and sensitivity of queueing networks. New York: Wiley, 1986.

M, Jenkins Theodore, Nelson Walter R. 1937-, and Rindi Alessandro, eds. Monte Carlo transport of electrons and photons. New York: Plenum Press, 1988.

Rennison, Andrew. Comparing alternative output-gap estimators: A Monte Carlo approach. Ottawa: Bank of Canada, 2003.

Ivashchenko, V. M. Modelirovanie kineticheskikh i͡a︡vleniĭ v poluprovodnikakh: Metod Monte-Karlo. Kiev: Nauk. dumka, 1990.

Rubinstein, Reuven Y. Monte Carlo optimization, simulation, and sensitivity of queuing networks. Malabar, Fla: Krieger Pub. Co., 1992.

Taniguchi International Symposium on the Theory of Condensed Matter (9th 1986 Susono-shi, Japan). Quantum Monte Carlo methods in equilibrium and nonequilibrium systems: Proceedings of the Ninth Taniguchi International Symposium, Susono, Japan, November 14-18, 1986. Edited by Suzuki M. 1937-. Berlin: Springer-Verlag, 1987.

Headrick, Todd C. Statistical simulation: Power method polynomials and other transformations. Boca Raton: Chapman & Hall/CRC, 2010.

Headrick, Todd C. Statistical simulation: Power method polynomials and other transformations. Boca Raton: Chapman & Hall/CRC, 2010.

Allen, Nancy L. Application of the Mantel-Haenszel procedure to complex samples of items. Princeton, N.J: Educational Testing Service, 1995.

Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer Academic Publishers, 1997.

Dimov, Ivan T. Monte Carlo methods for applied scientists. Hackensack, N.J: World Scientific, 2008.

Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.

1937-, Serra Daniel, ed. Bo yi lun yu jing ji xue: Game theory & economics / Christian Montet, Daniel Serra. Beijing: Jing ji guan li chu ban she, 2005.

Leppänen, Jaakko. Development of a new Monte Carlo reactor physics code. [Espoo, Finland]: VTT Technical Research Centre of Finland, 2007.

Introduction to Monte Carlo methods for transport and diffusion equations. Oxford: Oxford University Press, 2003.

Suzuki, Masuo. Quantum Monte Carlo Methods in Equilibrium and Nonequilibrium Systems: Proceedings of the Ninth Taniguchi International Symposium, Susono, Japan, November 14-18, 1986. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987.

I︠A︡roshchuk, I. O. Statisticheskoe modelirovanie rasprostranenii︠a︡ voln vo fluktuirui︠u︡shchikh sredakh. Vladivostok: Dalʹnauka, 2000.

George, Casella, and SpringerLink (Online service), eds. Introducing Monte Carlo Methods with R. New York, NY: Springer Science+Business Media, LLC, 2010.

Niederreiter, Harald. Monte Carlo and Quasi-Monte Carlo Methods 2002: Proceedings of a Conference held at the National University of Singapore, Republic of Singapore, November 25-28, 2002. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

Srivastava, M. S. Classification with a preassigned error rate when two covariance matrices are equal. Toronto: University of Toronto, Dept. of Statistics, 1998.

Zhao, Zhong. Sensitivity of propensity score methods to the specifications. Bonn, Germany: IZA, 2005.

Racine, J. S. The semiparametric approach to the estimation of systems of equations models in the presence of heteroskedasticity of unknown form. Toronto, Ont: Dept. of Economics, York University, 1989.

Freitas, Lopes Hedibert, ed. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. 2nd ed. Boca Raton: Taylor & Francis, 2006.

Gamerman, Dani. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.

Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.

Kolarova, Vasilena. Interartistichnii︠a︡t fenomen: Prez "Opiti" na Monten, "Mobile" na Bi︠u︡tor, "Katalonskata gradina" na Bi︠u︡tor i Baden... [s.l.]: Prostranstvo & forma, 2007.

Nightingale, M. P. Monte Carlo optimization of trial wave functions in quantum mechanics and statistical mechanics. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

Feiveson, Alan H. Improving the chi-squared approximation for bivariate normal tolerance regions. Washington, D. C: NASA, 1993.

E, Alcouffe Raymond, Los Alamos National Laboratory, and France. Commissariat à l'énergie atomique., eds. Monte-Carlo methods and applications in neutronics, photonics, and statistical physics: Proceedings of the joint Los Alamos National Laboratory-Commissariat à l'energie atomique Meeting held at Cadarache Castle, Provence, France, April 22-26, 1985. Berlin: Springer-Verlag, 1985.

Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Toronto, Ont: Dept. of Economics, York University, 1989.

Satō, Akira. Introduction to practice of molecular simulation: Molecular dynamics, Monte Carlo, Brownian dynamics, Lattice Boltzmann, dissipative particle dynamics. Amsterdam: Elsevier, 2011.

Hoogland, Jiri Kamiel. Radiative corrections, quasi-Monte Carlo methods and discrepancy: Computational aspects of high energy phenomenology. [Amsterdam: Universiteit van Amsterdam, 1996.

Schwenzfeger, K. J. Comparison of ERS-1 scatterometer Monte Carlo performance simulations using a weighted nonlinear least-squares and a maximum likelihood estimation method. Neubiberg: Hochschule der Bundeswehr München, 1985.

1947-, Gianola Daniel, ed. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag, 2002.

Bilanovic, Z. Neutron-photon energy deposition in CANDU reactor fuel channels: A comparison of modelling techniques using ANISN and MCNP computer codes. Chalk River, Ont: System Chemistry and Corrosion Branch, Chalk River Laboratories, 1994.

Querlioz, Damien. The Wigner Monte-Carlo method for nanoelectronic devices: Particle description of quantum transport and decoherence. London: ISTE, 2010.

Philippe, Dollfus, ed. The Wigner Monte-Carlo method for nanoelectronic devices: Particle description of quantum transport and decoherence. London: ISTE, 2010.

Field theory concepts: Electromagnetic fields, Maxwell's equations, grad, curl, div, etc. : finite-element method, finite-difference method, charge simulation method, Monte Carlo method. Berlin: Springer-Verlag, 1988.

1955-, Rubino Gerardo, and Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Hoboken, N.J: Wiley, 2009.

Monte Carlo Particle Transport Methods. Taylor & Francis Group, 2017.

Strategies for Quasi-Monte Carlo. Springer, 2011.

Haghighat, Alireza. Monte Carlo Methods for Particle Transport. Taylor & Francis Group, 2020.

Monte Carlo Methods for Particle Transport. Taylor & Francis Group, 2014.

Monte Carlo Methods for Particle Transport. Taylor & Francis Group, 2020.

Haghighat, Alireza. Monte Carlo Methods for Particle Transport. Taylor & Francis Group, 2020.