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Adaptive TreeHive: Ensemble of trees for enhancing imbalanced intrusion classification. [PDF]

PLoS One
Sobhani ME, Rodela AT, Farid DM.
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Adaptive Importance Sampling Message Passing

2022 IEEE International Symposium on Information Theory (ISIT), 2022
The aim of Probabilistic Programming (PP) is to automate inference in probabilistic models. One efficient realization of PP-based inference concerns variational message passing-based (VMP) inference in a factor graph. VMP is efficient but in principle only leads to closed-form update rules in case the model consists of conjugate and/or conditionally ...
Akbayrak, Semih, Şenöz, Ismail, de Vries, Bert   +2 more
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Adaptive mixture importance sampling

Journal of Statistical Computation and Simulation, 1998
Importance sampling involves approximation of functionals (such as expectations) of a target distribution by sampling from a design distribution. In many applications, it is natural or convenient to use a design distribution which is a mixture of given distributions.
Nandini Raghavan, Dennis D. Cox
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Safe adaptive importance sampling: A mixture approach

The Annals of Statistics, 2021
Adaptive importance sampling (AIS) constitutes new samples, such as particles in statistical physics, generated under certain probability distribution called policy \(q_k\) and the next policy \(q_{k+1}\) uses the new particles adaptively. In the earlier works, the policy is chosen as the kernel density estimate based on the previous particles ...
Delyon, Bernard, Portier, François
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Anti-tempered layered adaptive importance sampling

2017 22nd International Conference on Digital Signal Processing (DSP), 2017
Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling (IS) and Markov Chain Monte Carlo (MCMC) approaches.
Martino, Luca, Elvira, Víctor, Luengo, David   +2 more
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Parallel interacting Markov adaptive importance sampling

2015 23rd European Signal Processing Conference (EUSIPCO), 2015
Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the ...
Martino L., Elvira V., Luengo D., Corander J.   +3 more
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