A hitting-set generator (HSG) is a polynomial map $G:\mathbb{F}^k \to \mathbb{F}^n$ such that for all $n$-variate polynomials $C$ of small enough circuit size and degree, if nonzero, then $C\circ G$ nonzero. In this paper, we give new construction an HSG assuming have explicit sufficient hardness. Formally, prove the following over any field characteristic zero: Let $k\in \mathbb{N}$ $\delta ...

In this paper, we propose a matrix-variate generalization of the Gauss hypergeometric distribution and study several of its properties. We also derive probability density functions of the product of two independent random matrices when one of them is Gauss hypergeometric. These densities are expressed in terms of Appell’s first hypergeometric function F1 and Humbert’s confluent hypergeometric f...

In this paper, we applied the standard Monte Carlo, antithetic variate, and control variates methods to value double barrier knock-in option price. The underlying asset used in calculation of is share ANTM from April 1, 2019 until March 2022. simulated using methods. results showed that all converge exact solution, with variate method be fastest. Standard Carlo has least computational time, fol...

High-dimensional data is becoming more prevalent with new technologies in biomedical sciences, imaging and the Internet. Many examples of this data often contain complex relationships between and among sets of variables. When arranged in the form of a matrix, this data is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we prese...

In this paper, we describe a new speaker adaptation method based on the matrix-variate distribution of training models. A set of mean vectors of hidden Markov models (HMMs) is assumed to be drawn from the matrix-variate normal distribution, and bases are derived under this assumption. The resulting bases have the same dimension as that of the eigenvoice, thus adaptation can be performed using t...

Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-g...

Our article provides a study on the use and improvement of Hull and White’s (1988) control variate technique in pricing options. It contributes to the literature in two ways. Firstly we show that it is not optimal to use the entire error of a control variate against its known price (usually a closed-form solution) to correct and improve the unknown error of the unknown price of a complex option...

Recent approaches to text classi cation have used two di erent rst order probabilistic models for classi ca tion both of which make the naive Bayes assumption Some use a multi variate Bernoulli model that is a Bayesian Network with no dependencies between words and binary word features e g Larkey and Croft Koller and Sahami Others use a multinomial model that is a uni gram language model with i...