The automatic Algorithm ARI developed in this paper can generate variates from a large class of unimodal discrete distributions. It is only necessary to know the mode of the distribution and to have a subprogram available that can evaluate the probabilities. In a set up step the algorithm constructs a table mountain shaped hat function. Then rejection inversion, a new variant of the rejection m...

Standard Monte Carlo simulation needs prohibitive time to achieve reasonable estimations. for untractable integrals (i.e. multidimensional integrals and/or intergals with complex integrand forms). Several statistical technique, called variance reduction methods, are used to reduce the simulation time. In this note, we propose a generalization of the well known antithetic variate method. Princip...

Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing complex social phenomena. We generalize ERGM by also modeling nodal attributes as random variates, thus creating a random model of the full network, ...

The sum of correlated gamma random variables appears in the analysis of many wireless communications systems, e.g. in systems under Nakagami-m fading. In this Letter we obtain exact expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the sum of arbitrarily correlated gamma variables in terms of certain Lauricella functions. Index Terms Gamma ...

An urn contains black and red balls. Let Zn be the proportion of black balls at time n and 0 ≤ L < U ≤ 1 random barriers. At each time n, a ball bn is drawn. If bn is black and Zn−1 < U , then bn is replaced together with a random number Bn of black balls. If bn is red and Zn−1 > L, then bn is replaced together with a random number Rn of red balls. Otherwise, no additional balls are added, and ...

We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) approximations to jointly sample the current state of the Markov Chain and estimate conditional expectations, possibly by incorporating a full range of variance reduction techniques. We compute Rao-Blackwellized estimates ...

SSJ is a Java library for stochastic simulation, developed in the Département d'Informa-tique et de Recherche Opérationnelle (DIRO), at the Université de Montréal. It provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting statist...

Sufficient dimension reduction methods provide effective ways to visualize discriminant analysis problems. For example, Cook and Yin (2001) showed that the dimension reduction method of sliced average variance estimation (save) identifies variates that are equivalent to a quadratic discriminant analysis (qda) solution. This article makes this connection explicit to motivate the use of save vari...

We present the first adaptive random directions Newton algorithm under i.i.d., symmetric, uniformly distributed perturbations for a general problem of optimization under noisy observations. We also present a simple gradient search scheme under the aforementioned perturbation random variates. Our Newton algorithm requires generating N perturbation variates and three simulations at each iteration...