Search Results - "Mingas, Grigorios"

Particle MCMC algorithms and architectures for accelerating inference in state-space models by Mingas, Grigorios, Bottolo, Leonardo, Bouganis, Christos-Savvas

“…•Novel algorithmic and hardware techniques for fast SSM inference are proposed.•New algorithm extends applicability of particle MCMC to multi-modal…”
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An Unbiased MCMC FPGA-Based Accelerator in the Land of Custom Precision Arithmetic by Shuanglong Liu, Mingas, Grigorios, Bouganis, Christos-Savvas

“…Markov Chain Monte Carlo (MCMC) based methods have been the main tool used for Bayesian Inference by practitioners and researchers due to their flexibility and…”
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Population-Based MCMC on Multi-Core CPUs, GPUs and FPGAs by Mingas, Grigorios, Bouganis, Christos-Savvas

“…Markov Chain Monte Carlo (MCMC) is a method to draw samples from a given probability distribution. Its frequent use for solving probabilistic inference…”
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An FPGA implementation of the SMG-SLAM algorithm by Mingas, Grigorios, Tsardoulias, Emmanouil, Petrou, Loukas

“…One of the main tasks of a mobile robot in an unknown environment is to build and update a map of the environment and simultaneously determine its location…”
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Algorithms and architectures for mcmc acceleration in fpgas by Mingas, Grigorios

“…Markov Chain Monte Carlo (MCMC) is a family of stochastic algorithms which are used to draw random samples from arbitrary probability distributions. This task…”
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Multilevel Delayed Acceptance MCMC by Lykkegaard, Mikkel B, Dodwell, Tim J, Fox, Colin, Mingas, Grigorios, Scheichl, Robert

“…We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an…”
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Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3 by Lykkegaard, Mikkel B, Mingas, Grigorios, Scheichl, Robert, Fox, Colin, Dodwell, Tim J

“…Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood…”
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Faking feature importance: A cautionary tale on the use of differentially-private synthetic data by Giles, Oscar, Hosseini, Kasra, Mingas, Grigorios, Strickson, Oliver, Bowler, Louise, Smith, Camila Rangel, Wilde, Harrison, Lim, Jen Ning, Mateen, Bilal, Amarasinghe, Kasun, Ghani, Rayid, Heppenstall, Alison, Lomax, Nik, Malleson, Nick, O'Reilly, Martin, Vollmerteke, Sebastian

“…Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications,…”
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An exact MCMC accelerator under custom precision regimes by Shuanglong Liu, Mingas, Grigorios, Bouganis, Christos-Savvas

“…Markov chain Monte Carlo (MCMC) is one of the most popular and important tools to generate random samples from probability distributions over many variables…”
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Parallel resampling for particle filters on FPGAs by Shuanglong Liu, Mingas, Grigorios, Bouganis, Christos-Savvas

“…Particle filters (PFs) are a set of algorithms that implement recursive Bayesian filtering, which represent the posterior distribution by a set of weighted…”
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On Optimizing the Arithmetic Precision of MCMC Algorithms by Mingas, Grigorios, Rahman, Farhan, Bouganis, Christos-Savvas

“…Markov Chain Monte Carlo (MCMC) is an ubiquitous stochastic method, used to draw random samples from arbitrary probability distributions, such as the ones…”
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Towards heterogeneous solvers for large-scale linear systems by Venieris, Stylianos I., Mingas, Grigorios, Bouganis, Christos-Savvas

“…Applying Linear Regression to systems with a massive amount of observations, a scenario which is becoming increasingly common in the era of Big Data, poses…”
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A Custom Precision Based Architecture for Accelerating Parallel Tempering MCMC on FPGAs without Introducing Sampling Error by Mingas, G., Bouganis, C-S

“…Markov Chain Monte Carlo (MCMC) is a method used to draw samples from probability distributions in order to estimate - otherwise intractable - integrals. When…”
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