Tags: Bayesian statistics

All Categories (1-20 of 23)

  1. Autonomous Neutron Diffraction Experiments with ANDiE

    14 Nov 2021 | | Contributor(s):: Austin McDannald

    This tutorial will cover the working principles of ANDiE, how physics was encoded into the design, and demonstrate how ANDiE can be used to autonomously control neutron diffraction experiments.

  2. Autonomous Neutron Diffraction Explorer

    01 Nov 2021 | | Contributor(s):: Austin McDannald

    Autonomously control neutron diffraction experiments to discover order parameter.

  3. Batch Reification Fusion Optimization (BAREFOOT) Framework

    09 Jun 2021 | | Contributor(s):: Richard Couperthwaite

    This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of...

  4. A Batch Reification/Fusion Optimization Framework for Bayesian-based Material Optimization

    27 Apr 2021 | | Contributor(s):: Richard Couperthwaite, Raymundo Arroyave

    This tool is a Bayesian optimization framework that allows for a combination of a multi-fidelity (Reification/Fusion) optimization approach with a Batch Bayesian Approach.

  5. Uncertainty Quantification and Scientific Machine Learning for Complex Engineering Systems

    17 Aug 2020 | | Contributor(s):: Guang Lin

    In this talk, I will first present a review of the novel UQ techniques I developed to conduct stochastic simulations for very large-scale complex systems.

  6. ECE 595ML Lecture 13.2: Connecting Bayesian with Linear Regression

    24 Mar 2020 | | Contributor(s):: Stanley H. Chan

  7. ECE 595ML Lecture 12.2: Bayesian Parameter Estimation - Choosing Priors

    18 Mar 2020 | | Contributor(s):: Stanley H. Chan

  8. ECE 595ML Lecture 9.2: Bayesian Decision - Basic Principle

    29 Feb 2020 | | Contributor(s):: Stanley H. Chan

  9. ECE 595ML Lecture 9.3: Bayesian Decision - The Three Cases

    29 Feb 2020 | | Contributor(s):: Stanley H. Chan

  10. ECE 595ML Lecture 13.1: Connecting Bayesian with Linear Regression - Linear Regression Review

    17 Feb 2020 | | Contributor(s):: Stanley H. Chan

  11. ECE 595ML Lecture 12.1: Bayesian Parameter Estimation - Basic Principles

    14 Feb 2020 | | Contributor(s):: Stanley H. Chan

  12. Human-Interpretable Concept Learning via Information Lattices

    23 May 2019 | | Contributor(s):: Lav R. Varshney

    The basic idea is an iterative discovery algorithm that has a student-teacher architecture and that operates on a generalization of Shannon’s information lattice, which itself encodes a hierarchy of abstractions and is algorithmically constructed from group-theoretic foundations.

  13. Magnetic Tunnel Junction (MTJ) as Stochastic Neurons and Synapses: Stochastic Binary Neural Networks, Bayesian Inferencing, Optimization Problems

    26 Oct 2018 | | Contributor(s):: Abhronil Sengupta, Kaushik Roy

    In this presentation, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the units of such computing...

  14. ME 597UQ Lecture 24: Bayesian Model Comparison using Sequential Monte Carlo

    27 Apr 2018 | | Contributor(s):: Ilias Bilionis

  15. Efficient Exploration of Quantified Uncertainty in Granular Crystals

    16 Mar 2016 | | Contributor(s):: Juan Camilo Lopez

    This work presents a way of quantifying uncertainty in granular crystals in a computationally efficient way. To accomplish this, a low dimensional response surface is approximated through the method of active subspaces. Within this framework, special structure within the inputs is exploited to...

  16. Granular Crystals

    22 Jul 2015 | | Contributor(s):: Juan Camilo Lopez, Rohit Kaushal Tripathy, Ilias Bilionis, Marcial Gonzalez

    Allows the user to sample properties of granular chains under uncertainty propagation

  17. Cluster Optimization BGO 01

    29 Jul 2015 | | Contributor(s):: Ilias Bilionis, Yinuo Li

    Cluster Optimization Using Bayesian Global Optimization

  18. Gaussian process regression in 1D

    26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

  19. IMA 2013 UQ: Bayesian Approaches for Spatial- Stochastic Basis Selection: Applications to Fuel Cell Predictive Modeling

    13 Mar 2014 | | Contributor(s):: Guang Lin

    In this talk, two fully Bayesian methods (Bayesian uncertainty method and Bayesian mixture procedure) will be introduced that can evaluate generalized Polynomial Chaos (gPC) expansions in both stochastic and spatial domains when the number of the available basis functions is significantly larger...

  20. [Illinois] Intravoxel Incoherent Motion (IVIM) for Quantification of Microcirculatory Flow

    26 Feb 2014 | | Contributor(s):: Alex Cerjanic

    Diffusion weighted Magnetic Resonance Imaging (DW-MRI) is an increasingly popular tool used to probe the microscopic structure of materials and biological tissues. Most studies employing DW-MRI rely on the passive diffusion of water molecules to interrogate the structure of the object or tissue...