Almtract---Our new sequential and parallel algorithms establish new record upper bounds on both arithmetic and Boolean complexity of approximating to complex polynomial zeros. O(n 2 log b log n) arithmetic operations or O(n log n log (bn)) parallel steps and n log b/log (bn) processors suffice in order to approximate with absolute errors ~< 2 m-b to all the complex zeros of an nth degree polyno...

Methods of Approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is over-coming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampling. A number of recent developments aim to address this with extensions based on sequential Monte Carlo (SMC) strategies. We build on this h...

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We concentrate on a family of discrete event systems obtained from a simple modular design principle that include in a controlled way primitives to deal with concurrency, decisions, synchronization, blocking, and bulk movements of jobs. Due to the functional complexity of such systems, reliable throughput approximation algorithms must be deeply supported on a structure based decomposition techn...

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dy...

Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Effective SMC algorithms that are currently available for ABC have a computational c...

In this paper, we propose a probabilistic model to study the interaction of bidder and seller agents in sequential automated auctions. We consider a designated “special bidder” (SB) who arrives at an auction and observes the ongoing activities among a number of bidders and the seller jointly, as a stochastic system that is parameterised by the rate of the bidding and selling events. The auction...

In this paper, we consider the noncoherent code acquisition problem using sequential schemes. Since code acquisition schemes using the maximum likelihood estimate usually require a high level of computational complexity, simplified schemes are proposed and analyzed based on approximations. The performance of the simplified and original code acquisition schemes are compared in additive white Gau...

In the last decade, sequential Monte-Carlo methods (SMC) emerged as a key tool in computational statistics (see for instance Doucet et al. (2001), Liu (2001), Künsch (2001)). These algorithms approximate a sequence of distributions by a sequence of weighted empirical measures associated to a weighted population of particles. These particles and weights are generated recursively according to ele...