scholarly journals An Auto-validating Rejection Sampler for Differentiable Arithmetical Expressions: Posterior Sampling of Phylogenetic Quartets

2014 ◽  
pp. 143-152
Author(s):  
Raazesh Sainudiin
Keyword(s):  
Posterior Sampling

scholarly journals Non-Gaussian inference from non-linear and non-Poisson biased distributed data

2014 ◽  
Vol 10 (S306) ◽  
pp. 258-261
Author(s):  
Metin Ata ◽  
Francisco-Shu Kitaura ◽  
Volker Müller
Keyword(s):  
Dark Matter ◽  
Statistical Inference ◽  
Computer Code ◽  
Matter Density ◽  
Density Field ◽  
Distributed Data ◽  
Dark Matter Density ◽  
Posterior Sampling ◽  
Non Linear ◽  
Non Gaussian

AbstractWe study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based onN-body simulations.

scholarly journals Fiducial and Posterior Sampling

2015 ◽  
Vol 44 (17) ◽  
pp. 3754-3767 ◽  
Author(s):  
Gunnar Taraldsen ◽  
Bo H. Lindqvist
Keyword(s):  
Posterior Sampling

scholarly journals Cardiovascular Modeling With Adapted Parametric Inference

2018 ◽  
Vol 62 ◽  
pp. 91-107 ◽  
Author(s):  
Didier Lucor ◽  
Olivier P. Le Maître
Keyword(s):  
Approximation Error ◽  
Surrogate Models ◽  
Patient Specific ◽  
System Response ◽  
Cardiovascular Parameter ◽  
Monte Carlo Algorithms ◽  
Statistical Framework ◽  
Posterior Sampling ◽  
Powerful Approach ◽  
Parameter Values

Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.

scholarly journals Posterior sampling from $\varepsilon$-approximation of normalized completely random measure mixtures

2016 ◽  
Vol 10 (2) ◽  
pp. 3516-3547 ◽  
Author(s):  
Raffaele Argiento ◽  
Ilaria Bianchini ◽  
Alessandra Guglielmi
Keyword(s):  
Random Measure ◽  
Posterior Sampling

scholarly journals Bayesian optimization with partially specified queries

2022 ◽  
Author(s):  
Shogo Hayashi ◽  
Junya Honda ◽  
Hisashi Kashima
Keyword(s):  
Black Box ◽  
Bayesian Optimization ◽  
Unknown Input ◽  
Bandit Problem ◽  
Test Functions ◽  
Significant Cost ◽  
Posterior Sampling ◽  
Regret Bounds ◽  
Input Variables ◽  
Real World Datasets

AbstractBayesian optimization (BO) is an approach to optimizing an expensive-to-evaluate black-box function and sequentially determines the values of input variables to evaluate the function. However, it is expensive and in some cases becomes difficult to specify values for all input variables, for example, in outsourcing scenarios where production of input queries with many input variables involves significant cost. In this paper, we propose a novel Gaussian process bandit problem, BO with partially specified queries (BOPSQ). In BOPSQ, unlike the standard BO setting, a learner specifies only the values of some input variables, and the values of the unspecified input variables are randomly determined according to a known or unknown distribution. We propose two algorithms based on posterior sampling for cases of known and unknown input distributions. We further derive their regret bounds that are sublinear for popular kernels. We demonstrate the effectiveness of the proposed algorithms using test functions and real-world datasets.

scholarly journals Clustering Results Interpretation of Continuous Variables Using Bayesian Inference

2021 ◽  
Author(s):  
Ksenia Balabaeva ◽  
Sergey Kovalchuk
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Inference ◽  
Clinical Pathways ◽  
Posterior Distributions ◽  
Continuous Variables ◽  
Posterior Sampling ◽  
Binary Features

The present study is devoted to interpretable artificial intelligence in medicine. In our previous work we proposed an approach to clustering results interpretation based on Bayesian Inference. As an application case we used clinical pathways clustering explanation. However, the approach was limited by working for only binary features. In this work, we expand the functionality of the method and adapt it for modelling posterior distributions of continuous features. To solve the task, we apply BEST algorithm to provide Bayesian t-testing and use NUTS algorithm for posterior sampling. The general results of both binary and continuous interpretation provided by the algorithm have been compared with the interpretation of two medical experts.

Sign in / Sign up

Export Citation Format

Share Document