Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
Sensitivity Analysis for Financial Models in Excel
If you like this video, drop a comment, give it a thumbs up and consider subscribing here: https://www.youtube.com/c/HowToBeAnAdult?sub_confirmation=1 Music from: YouTube Audio Library Check out our new project: https://magnimetrics.com The Online Platform for Automated Analysis Random
From playlist Excel Tutorials
Stefano Marelli: Metamodels for uncertainty quantification and reliability analysis
Abstract: Uncertainty quantification (UQ) in the context of engineering applications aims aims at quantifying the effects of uncertainty in the input parameters of complex models on their output responses. Due to the increased availability of computational power and advanced modelling tech
From playlist Probability and Statistics
Uncertainty Estimation via (Multi) Calibration
A Google TechTalk, presented by Aaron Roth, 2020/10/02 Paper Title: "Moment Multi-calibration and Uncertainty Estimation" ABSTRACT: We show how to achieve multi-calibrated estimators not just for means, but also for variances and other higher moments. Informally, this means that we can fi
From playlist Differential Privacy for ML
Introduction to Regression Analysis
This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.
From playlist Performing Linear Regression and Correlation
04-3 Sensitivity Analysis Trees
Sensitivity analysis using classification and regression trees
From playlist QUSS GS 260
14 Data Analytics: Indicator Methods
Lecture on the use of indicators for spatial estimation and simulation.
From playlist Data Analytics and Geostatistics
DDPS | Parameter Subset Selection and Active Subspace Techniques for Engineering & Biological Models
Engineering and biological models generally have a number of parameters which are nonidentifiable in the sense that they are not uniquely determined by measured responses. Furthermore, the computational cost of high-fidelity simulation codes often precludes their direct use for Bayesian m
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
From playlist Plenary talks One World Symposium 2020
A short tutorial on differential privacy: Dr Borja Balle, Amazon Research
Differential privacy is a robust mathematical framework for designing privacy-preserving computations on sensitive data. In this tutorial we will cover the key definitions and intuitions behind differential privacy and introduce the core building blocks used by most differentially private
From playlist Turing Seminars
Data Science - Part XV - MARS, Logistic Regression, & Survival Analysis
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview on extending the regression concepts brought forth in previous lectures. We wi
From playlist Data Science
On the Convergence of Deep Learning with Differential Privacy
A Google TechTalk, presented by Zhiqi Bu, 2021/07/02 ABSTRACT: Differential Privacy for ML Series. In deep learning with differential privacy (DP), the neural network achieves the privacy usually at the cost of slower convergence (and thus lower performance) than its non-private counterpa
From playlist Differential Privacy for ML
Variational Bayes: An Overview and Risk-Sensitive Formulations by Harsha Honnappa
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Stanford CS229: Machine Learning | Summer 2019 | Lecture 21 - Evaluation Metrics
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3b2QxDe Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html 0:00
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Deciphering the discovery: Astrophysical implications of GW150914 by John Veitch
The Future of Gravitational-Wave Astronomy URL: http://www.icts.res.in/discussion_meeting/fgwa2016/ DATES: Monday 04 Apr, 2016 - Friday 08 Apr, 2016 VENUE : Ramanujan Lecture Hall, ICTS Bangalore DESCRIPTION: On 2016 February 11, two major international collaborations jointly announce
From playlist The Future of Gravitational-Wave Astronomy
Risk Management of Option Books with Arbitrage-Free Neural-SDE Market Models (SIAM FME)
SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spe
From playlist SIAM Activity Group on FME Virtual Talk Series
What is Curve Fitting Toolbox? - Curve Fitting Toolbox Overview
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Fit curves and surfaces to data using regression, interpolation, and smoothing using Curve Fitting Toolbox. For more videos, visit http://www.mathworks.com/products/curvefi
From playlist Math, Statistics, and Optimization
Sylvia Biscoveanu - Power Spectral Density Uncertainty and Gravitational-Wave Parameter Estimation
Recorded 19 November 2021. Sylvia Biscoveanu of the Massachusetts Institute of Technology presents "The Effect of Power Spectral Density Uncertainty on Gravitational-Wave Parameter Estimation" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy