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- Monte Carlo
- 2016

Title Type monte carlo methods in financial engineering stochastic modelling and applied probability v 53 PDF stochastic simulation algorithms and analysis stochastic modelling and applied probability no 57 no 100 PDF adaptive algorithms and stochastic approximations stochastic modelling and applied probability PDF discrete-time markov chains two-timescale… (More)

- Monte Carlo, E. J. Maginn, J. K. Shah, George Louis, LeClerc Comte de Buffon
- 2011

- Mike West, Monte Carlo
- 1995

Dynamic linear models with time-varying cyclical components are developed for the analysis of times series with persistent though time-varying cyclical behaviour. The development covers inference on wavelengths of possibly several persistent cycles in non-stationary times series, permitting explicit time variation in amplitudes and phases of component… (More)

We review the across-model simulation approach to computation for Bayesian model determination , based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods… (More)

Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. The sample is collected through a form of snowball sampling where current sample members recruit future sample members. In this paper we observe that respondent-driven sampling can be viewed as Markov chain Monte… (More)

- Monte Carlo, Tree Search, H S Chang, J Hu, M C Fu, S I Marcus
- 2017

Students will present chapters from the text, as well as additional papers that will be supplied. This seminar will focus on topics related to simulation-based algorithms for Markov decision processes, multi-armed bandit problems, Monte Carlo tree search, with applications including Google DeepMind's AlphaGo. Students will receive either 1 or 2 credits,… (More)

- L Prihodko, A S Denning, N P Hanan, I Baker, K Davis, Monte Carlo
- 2008

GLUE SiB WLEF a b s t r a c t This paper explores the use of Monte Carlo carbon cycle data assimilation within the generalized likelihood uncertainty estimation (GLUE) framework to evaluate the sensitivities of a well-known complex land surface model (SiB v2.5) to its parameterization and the predictive uncertainty of simulated fluxes on a monthly basis,… (More)

The aims of Monte Carlo methods are to solve one or both of the following problems. Problem 1: to generate samples fx (r) g R r=1 from a given probability distribution P(x). Problem 2: to estimate expectations of functions under this distribution, for example = h(x)i Z d N x P(x)(x): (11.1) The probability distribution P(x), which we will call the target… (More)

- Alina MOMOT, KOMPUTEROWYCH I METOD, MONTE CARLO
- 2010

Design and testing of investment strategies is greatly facilitated by using computer simulations. Monte Carlo method is often used for mathematical model-ing of processes occurring in the financial markets. This article presents a complex investment strategy based on the simultaneous use of several basic strategies and its alternative using random switching… (More)

- Monte Carlo, Y Y Y Y Y Y Abb Gridview, +8 authors N=no
- 2003

Executive Summary KEMA Consulting has assessed existing commercial tools in the U.S. market that might be used to help support future DOE assessments of national interest transmission bottlenecks, consistent with recommendations in the DOE National Transmission Grid Study. The tool or tools must be capable of supporting studies of power systems… (More)