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pro vyhledávání: '"Labadi, Luai Al"'
ROC analyses are considered under a variety of assumptions concerning the distributions of a measurement $X$ in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions.
Externí odkaz:
http://arxiv.org/abs/2103.00772
There has been an increasing trend of females performing better than males academically across the mathematical engineering courses. To confirm this assumption, final marks of two independent samples of students from Calculus courses across all field
Externí odkaz:
http://arxiv.org/abs/1907.00552
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
The beta process has recently been widely used as a nonparametric prior for different models in machine learning, including latent feature models. In this paper, we prove the asymptotic consistency of the finite dimensional approximation of the beta
Externí odkaz:
http://arxiv.org/abs/1411.3434
In recent years, Bayesian nonparametric statistics has gathered extraordinary attention. Nonetheless, a relatively little amount of work has been expended on Bayesian nonparametric hypothesis testing. In this paper, a novel Bayesian nonparametric app
Externí odkaz:
http://arxiv.org/abs/1411.3427
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
In this paper, we develop simple, yet efficient, procedures for sampling approximations of the two-Parameter Poisson-Dirichlet Process and the normalized inverse-Gaussian process. We compare the efficiency of the new approximations to the correspondi
Externí odkaz:
http://arxiv.org/abs/1209.5359
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
In this paper, we present some asymptotic properties of the normalized inverse-Gaussian process. In particular, when the concentration parameter is large, we establish an analogue of the empirical functional central limit theorem, the strong law of l
Externí odkaz:
http://arxiv.org/abs/1206.6658
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
Ferguson's Dirichlet process plays an important role in nonparametric Bayesian inference. Let $P_a$ be the Dirichlet process in $\mathbb{R}$ with a base probability measure $H$ and a concentration parameter $a>0.$ In this paper, we show that $\sqrt {
Externí odkaz:
http://arxiv.org/abs/1109.5261
Autor:
Zarepour, Mahmoud, Labadi, Luai Al
We describe a simple and efficient procedure for approximating the L\'evy measure of a $\text{Gamma}(\alpha,1)$ random variable. We use this approximation to derive a finite sum-representation that converges almost surely to Ferguson's representation
Externí odkaz:
http://arxiv.org/abs/1107.0521
Autor:
Labadi, Luai Al
Publikováno v:
Brazilian Journal of Probability and Statistics, 2015 Feb 01. 29(1), 53-70.
Externí odkaz:
https://www.jstor.org/stable/43601319
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
Publikováno v:
Sankhyā: The Indian Journal of Statistics, Series A (2008-), 2014 Feb 01. 76(1), 158-176.
Externí odkaz:
https://www.jstor.org/stable/44114226