Results 31 to 40 of about 149,758 (276)
Statistical Network Analysis with Bergm
Journal of Statistical Software, 2022 Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the complex ...
Alberto Caimo +3 more
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“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study
Entropy, 2021 We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models.
Yuliya Shapovalova
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Model averaging, optimal inference and habit formation
Frontiers in Human Neuroscience, 2014 Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines.
Thomas H B FitzGerald +2 more
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Sampling the Variational Posterior with Local Refinement
Entropy, 2021 Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is ...
Marton Havasi +4 more
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Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks
Applied Sciences, 2019 Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection).
Cheol Young Park +3 more
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Abismal: Approximate Bayesian Inference for Scaling and Merging at Advanced Lightsources [PDF]
Struct DynExperimental artifacts in diffraction data are typically corrected by an optimization algorithm known as scaling. After appropriate scaling, redundant measurements can then be merged.
Mai D, Hekstra D, Poitevin F, Dalton K.
europepmc +2 more sources
Mathematics, 2021 In this paper, statistical inference and prediction issue of left truncated and right censored dependent competing risk data are studied. When the latent lifetime is distributed by Marshall–Olkin bivariate Rayleigh distribution, the maximum likelihood ...
Ke Wu, Liang Wang, Li Yan, Yuhlong Lio
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Approximation enhancement for stochastic Bayesian inference
International Journal of Approximate Reasoning, 2017 Advancements in autonomous robotic systems have been impeded by the lack of a specialized computational hardware that makes real-time decisions based on sensory inputs. We have developed a novel circuit structure that efficiently approximates naïve Bayesian inference with simple Muller C-elements.
Friedman, Joseph +4 more
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A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms [PDF]
, 2018 The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community.
Cox, Marco +2 more
core +3 more sources
Kernel approximate Bayesian computation in population genetic inferences [PDF]
Statistical Applications in Genetics and Molecular Biology, 2013 Approximate Bayesian computation (ABC) is a likelihood-free approach for Bayesian inferences based on a rejection algorithm method that applies a tolerance of dissimilarity between summary statistics from observed and simulated data. Although several improvements to the algorithm have been proposed, none of these improvements avoid the following two ...
Nakagome, Shigeki +2 more
openaire +3 more sources