The problem of random sampling without replacement (RS) calls for the selection of m distinct random items out of a population of size n. If all items have the same probability to be selected, the problem is known as uniform RS. Uniform random sampling in one pass is discussed in [1, 5, 10]. Reservoir-type uniform sampling algorithms over data streams are discussed in [11]. A parallel uniform r...

We consider the problem of uniform sampling in large scale open systems. Uniform sampling is a fundamental primitive that guarantees that any individual in a population has the same probability to be selected as sample. An important issue that seriously hampers the feasibility of uniform sampling in open and large scale systems is the unavoidable presence of malicious nodes. In this paper we sh...

Over the last decade, importance sampling has been a popular technique for the efficient estimation of rare event probabilities. This paper presents an approach for applying balanced likelihood ratio importance sampling to the problem of quantifying the probability that the content of the second buffer in a two node tandem Jackson network reaches some high level before it becomes empty. Heurist...

Statistical inference is often justified by long-run properties of the sampling distributions, such as the repeated sampling rationale. These are frequentist justifications of statistical inference. I argue, in line with existing philosophical literature, but against a widespread image in empirical science, that these justifications are flawed. Then I propose a novel interpretation of probabili...

Thompson Sampling algorithm is a well known Bayesian algorithm for solving stochastic multi-armed bandit. At each time step the algorithm chooses each arm with probability proportional to it being the current best arm. We modify the strategy by introducing a paramter h which alters the importance of the probability of an arm being the current best arm. We show that the optimality of Thompson sa...

Probabilistic sequence models estimated from large corpora typically require smoothing techniques to reserve some probability mass for unobserved events. These techniques fail to distinguish between events unobserved due to sampling limitations, sampling zeros, and those unobserved due to structural reasons such as syntactic constraints, structural zeros. We investigate the use of statistical t...