DTSC - GTSA - Artículos de Revistashttp://hdl.handle.net/10016/90422016-09-25T19:07:21Z2016-09-25T19:07:21ZEfficient random variable generation: ratio of uniforms and polar rejection samplingLuengo García, DavidMartino, Lucahttp://hdl.handle.net/10016/166452013-10-01T00:03:58Z2012-03-01T00:00:00ZEfficient random variable generation: ratio of uniforms and polar rejection sampling
Luengo García, David; Martino, Luca
Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications.
2012-03-01T00:00:00ZEfficient sampling from truncated bivariate Gaussians via Box-Muller transformationMartino, LucaLuengo García, DavidMíguez Arenas, Joaquínhttp://hdl.handle.net/10016/166442013-10-01T00:05:07Z2012-11-01T00:00:00ZEfficient sampling from truncated bivariate Gaussians via Box-Muller transformation
Martino, Luca; Luengo García, David; Míguez Arenas, Joaquín
Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.
2012-11-01T00:00:00ZAlmost rejectionless sampling from Nakagami-m distributions (m≥1)Luengo García, DavidMartino, Lucahttp://hdl.handle.net/10016/166432013-10-01T00:06:38Z2012-11-01T00:00:00ZAlmost rejectionless sampling from Nakagami-m distributions (m≥1)
Luengo García, David; Martino, Luca
The Nakagami-m distribution is widely used for the simulation of fading channels in wireless communications. A novel, simple and extremely efficient acceptance-rejection algorithm is introduced for the generation of independent Nakagami-m random variables. The proposed method uses another Nakagami density with a half-integer value of the fading parameter, mp=n/2=m, as proposal function, from which samples can be drawn exactly and easily. This novel rejection technique is able to work with arbitrary values of m=1, average path energy, =, and provides a higher acceptance rate than all currently available methods.
2012-11-01T00:00:00ZA multi-point Metropolis scheme with generic weight functionsMartino, LucaPascual Del Olmo, VíctorRead, Jesse Michaelhttp://hdl.handle.net/10016/166412013-10-01T00:03:59Z2012-07-01T00:00:00ZA multi-point Metropolis scheme with generic weight functions
Martino, Luca; Pascual Del Olmo, Víctor; Read, Jesse Michael
The multi-point Metropolis algorithm is an advanced MCMC technique based on drawing several correlated samples at each step and choosing one of them according to some normalized weights. We propose a variation of this technique where the weight functions are not specified, i.e., the analytic form can be chosen arbitrarily. This has the advantage of greater flexibility in the design of high-performance MCMC samplers. We prove that our method fulfills the balance condition, and provide a numerical simulation. We also give new insight into the functionality of different MCMC algorithms, and the connections between them.
2012-07-01T00:00:00Z