Kernel density estimation via diffusion
- Z. Botev, J. Grotowski, Dirk P. Kroese
- Computer Science
- 1 October 2010
A new adaptive kernel density estimator based on linear diffusion processes that builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate and a new plug-in bandwidth selection method that is free from the arbitrary normal reference rules used by existing methods.
Handbook of Monte Carlo Methods
- Dirk P. Kroese, T. Taimre, Z. Botev
- Computer Science
- 15 March 2011
Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research.
The normal law under linear restrictions: simulation and estimation via minimax tilting
- Z. Botev
- Mathematics
- 14 March 2016
Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain…
Chapter 3 – The Cross-Entropy Method for Optimization
- Z. Botev, Dirk P. Kroese, R. Rubinstein, P. L'Ecuyer
- Computer Science, Biology
- 31 December 2013
Why the Monte Carlo method is so important today
- Dirk P. Kroese, T. Brereton, T. Taimre, Z. Botev
- Computer Science, Education
- 1 November 2014
The reasons why the Monte Carlo method has evolved from a ‘last resort’ solution to a leading methodology that permeates much of contemporary science, finance, and engineering are explored.
An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting
- Z. Botev, Dirk P. Kroese
- Mathematics, Computer Science
- 20 May 2008
A new adaptive simulation approach that does away with likelihood ratios, while retaining the multi-level approach of the cross-entropy method and allows one to sample exactly from the target distribution rather than asymptotically as in Markov chain Monte Carlo.
The Generalized Cross Entropy Method, with Applications to Probability Density Estimation
- Z. Botev, Dirk P. Kroese
- Computer Science, Mathematics
- 1 February 2011
A framework for density estimation is described which uses information-theoretic measures of model complexity with the aim of constructing a sparse density estimator that does not rely on large sample approximations.
Global likelihood optimization via the cross-entropy method with an application to mixture models
- Z. Botev, Dirk P. Kroese
- Computer ScienceProceedings of the Winter Simulation Conference…
- 5 December 2004
A new approach based on the cross-entropy (CE) method is presented, and its use for the analysis of mixture models is illustrated.
Spatial Process Simulation
- Dirk P. Kroese, Z. Botev
- Computer Science
- 2015
This chapter describes how to simulate realizations from the main types of spatial processes, including Gaussian and Markov random fields, point processes, spatial Wiener processes, and Levy fields.
Efficient Monte Carlo simulation via the generalized splitting method
- Z. Botev, Dirk P. Kroese
- Computer Science, MathematicsStatistics and computing
- 2012
A new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multiddimensional densities is described, inspired by the classical splitting method.
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