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
How to calculate margin of error and standard deviation
In this tutorial I show the relationship standard deviation and margin of error. I calculate margin of error and confidence intervals with different standard deviations. Playlist on Confidence Intervals http://www.youtube.com/course?list=EC36B51DB57E3A3E8E Like us on: http://www.facebook
From playlist Confidence Intervals
How to calculate sample size and margin of error
In this tutorial I show the relationship between sample size and margin of error. I calculate the margin of error and confidence interval using three different sample sizes. As the sample size increases the margin of error goes down. Like us on: http://www.facebook.com/PartyMoreStudyLes
From playlist Confidence Intervals
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
Percent Uncertainty In Measurement
This video tutorial provides a basic introduction into percent uncertainty. It also discusses topics such as estimated uncertainty, absolute uncertainty, and relative uncertainty. This video provides an example explaining how to calculate the percent uncertainty in the volume of the sphe
From playlist New Physics Video Playlist
DSI | Leakage and the Reproducibility Crisis in ML-based Science
The use of machine learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. In this talk, I will present results from our investigation of
From playlist DSI Virtual Seminar Series
Giacomo Dimarco: Numerical methods and uncertainty quantificationfor kinetic equations - lecture 1
In this course, we will consider the development and the analysis of numerical methods for kinetic partial differential equations. Kinetic equations represent a way of describing the time evolution of a system consisting of a large number of particles. Due to the high number of dimensions
From playlist CEMRACS 2022
Petros Koumoutsakos - Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics
Recorded 11 January 2023. Petros Koumoutsakos of Harvard University presents "Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: Over the last last thirty years we have experien
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
DDPS | Uncertainty quantification and deep learning for water-hazard prediction by Ajay Harish
Description: As a typhoon makes landfall, it can result in high waves, high winds and a region of low pressure. The difference in the observed and regular sea level can be attributed to this advancing typhoon and is known as storm surge. Such surge when combined with the waves can lead to
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
How to calculate Margin of Error Confidence Interval for a population proportion
In this tutorial I explain and then calculate, using an example, the margin of error and confidence interval for a population proportion. Like us on: http://www.facebook.com/PartyMoreStudyLess Link To Playlist on Confidence Intervals http://www.youtube.com/course?list=EC36B51DB57E3A3E8E
From playlist Confidence Intervals
Data-centric engineering in aero-engines: Pranay Seshadri, Cambridge
About the event Aero-engines are astonishing engineering feats. They are tasked with the efficient delivery of thrust—some generating as much as 400 kN—whilst adhering to stringent emission and safety regulations. From the centrifugal force acting on each fan blade (equivalent to the weig
From playlist Data-Centric Engineering Seminar Series
Bayesian inference and convex geometry: theory, methods, (...) - Pereyra - Workshop 2 - CEB T1 2019
Marcelo Pereyra (Heriot-Watt Univ.) / 14.03.2019 Bayesian inference and convex geometry: theory, methods, and algorithms. This talk summarises some new developments in theory, methods, and algorithms for performing Bayesian inference in high-dimensional models that are log-concave, with
From playlist 2019 - T1 - The Mathematics of Imaging
Some thoughts on Gaussian processes for emulation of deterministic computer models: Michael Stein
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes
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
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
A snapshot of few-shot classification - Richard Zemel
More videos on http://video.ias.edu
From playlist Mathematics
Giacomo Dimarco: Numerical methods and uncertainty quantificationfor kinetic equations - lecture 2
In this course, we will consider the development and the analysis of numerical methods for kinetic partial differential equations. Kinetic equations represent a way of describing the time evolution of a system consisting of a large number of particles. Due to the high number of dimensions
From playlist CEMRACS 2022
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments
The Data Science Institute (DSI) hosted a virtual seminar by Robert Gramacy from Virginia Tech on March 15, 2021. Read more about the DSI seminar series at https://data-science.llnl.gov/latest/seminar-series. We investigate the merits of replication and provide methods that search for opti
From playlist DSI Virtual Seminar Series
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 1: Bayesian analysis...
Bayesian inference and mathematical imaging - Part 1: Bayesian analysis and decision theory Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underp
From playlist Probability and Statistics