Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship ("model") to calculate that derived quantity. The model used to convert the measurements into the derived quantity is usually based on fundamental principles of a science or engineering discipline. The uncertainty has two components, namely, bias (related to accuracy) and the unavoidable random variation that occurs when making repeated measurements (related to precision). The measured quantities may have biases, and they certainly have random variation, so what needs to be addressed is how these are "propagated" into the uncertainty of the derived quantity. Uncertainty analysis is often called the "propagation of error." (Wikipedia).
Uncertainty and Propagation of Errors
A discussion of how to report experimental uncertainty, and how to calculate propagation of errors. Based on the nice video by paulcolor: https://youtu.be/V0ZRvvHfF0E, with some personal edits.
From playlist Experimental Physics
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
Uncertainty Principle - Klim Efremenko
Klim Efremenko Tel-Aviv University; Member, School of Mathematics April 23, 2013 Informally, uncertainty principle says that function and its Fourier transform can not be both concentrated. Uncertainty principle has a lot of applications in areas like compressed sensing, error correcting c
From playlist Mathematics
GCSE Working Scientifically "Uncertainty"
In this video, we look at uncertainty. First we explore what is meant by the idea of uncertainty and then we look at how to calculate uncertainty for a set of measurements.
From playlist GCSE Working Scientifically
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
Introduction to Estimation Theory
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.
From playlist Estimation and Detection Theory
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
Lecture Lorenzo Pareschi: Uncertainty quantification for kinetic equations I
The lecture was held within the of the Hausdorff Trimester Program: Kinetic Theory Abstract: In these lectures we overview some recent results in the field of uncertainty quantification for kinetic equations with random inputs. Uncertainties may be due to various reasons, like lack of kn
From playlist Summer School: Trails in kinetic theory: foundational aspects and numerical methods
Maxim Ziatdinov - Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy
Recorded 27 January 2023. Maxim Ziatdinov of the Oak Ridge National Laboratory presents "Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: Machine learning and artificial intelligence are
From playlist 2023 Learning and Emergence in Molecular Systems
DDPS | Uncertainty-aware guided wave structural health monitoring using ensemble learning
Uncertainty-aware guided wave structural health monitoring using ensemble learning by Ishan Khurjekar (University of Florida) Description: Monitoring the integrity of structures such as buildings, bridges, oilrigs, and airplanes among others, is crucial in today’s world. Indeed, the field
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
HL-LHC Physics Perspectives by Sezen Sekman
DISCUSSION MEETING HUNTING SUSY @ HL-LHC (ONLINE) ORGANIZERS Satyaki Bhattacharya (SINP, India), Rohini Godbole (IISc, India), Kajari Majumdar (TIFR, India), Prolay Mal (NISER-Bhubaneswar, India), Seema Sharma (IISER-Pune, India), Ritesh K. Singh (IISER-Kolkata, India) and Sanjay Kumar S
From playlist HUNTING SUSY @ HL-LHC (ONLINE) 2021
Scientific Seminar: First results from the Muon g-2 experiment at Fermilab
The first results from the Muon g-2 experiment at Fermilab were unveiled and discussed in a special seminar on April 7, 2021. The experimental result was presented by Chris Polly, Fermilab physicist and co-spokesperson for the Muon g-2 scientific collaboration, following a summary of the c
From playlist Muon g-2
Experimental overview of Flavor Physics and SUSY by Gagan Mohanty
DISCUSSION MEETING HUNTING SUSY @ HL-LHC (ONLINE) ORGANIZERS Satyaki Bhattacharya (SINP, India), Rohini Godbole (IISc, India), Kajari Majumdar (TIFR, India), Prolay Mal (NISER-Bhubaneswar, India), Seema Sharma (IISER-Pune, India), Ritesh K. Singh (IISER-Kolkata, India) and Sanjay Kumar S
From playlist HUNTING SUSY @ HL-LHC (ONLINE) 2021
Daniel Schwen - MOOSE: Parallel sampling for rare event probabilities & inverse Bayesian inference
Recorded 30 March 2023. Daniel Schwen of the Idaho National Laboratory presents "Parallel sampling for computing rare event probabilities, inverse Bayesian inference and, uncertainty quantification with MOOSE" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
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
Hunting SUSY @ HL-LHC (Theory outlook) by John Ellis
DISCUSSION MEETING HUNTING SUSY @ HL-LHC (ONLINE) ORGANIZERS Satyaki Bhattacharya (SINP, India), Rohini Godbole (IISc, India), Kajari Majumdar (TIFR, India), Prolay Mal (NISER-Bhubaneswar, India), Seema Sharma (IISER-Pune, India), Ritesh K. Singh (IISER-Kolkata, India) and Sanjay Kumar S
From playlist HUNTING SUSY @ HL-LHC (ONLINE) 2021
Results from BELLE II & LHCb by Diego Tonnelli
INFOSYS - ICTS CHANDRASEKHAR LECTURES LOOKING INTO THE FUTURE OF HIGH-ENERGY PARTICLE PHYSICS SPEAKER: Gian Giudice (CERN, Switzerland) VENUE: Ramanujan Lecture Hall, ICTS Campus Date & Time: Lecture 1: Monday, 21 November 2022 at 09:45 to 10:45 Lecture 2:
From playlist Particle Physics: Phenomena, Puzzles, Promises - (Edited)
Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning?
Recorded 25 January 2023. Kyunghyun Cho of New York University presents "Lab-in-the-loop de novo antibody design - what are we missing from machine learning?" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: In this talk, I will give an overview of what we do at Pr
From playlist 2023 Learning and Emergence in Molecular Systems
IB Chemistry Topic 11.1 Uncertainties and errors
IB Chemistry Topic 11.1 Uncertainties and errors How to calculation uncertainty using uncertainty propagation. Multiply and divide add percentage error. Plus and minus add raw uncertainties. Also calculations for percentage error and the half-way method for when uncertainty is not mainly
From playlist Topic 11/21 Measurement and data processing