Game theory | Expected utility
In decision theory and quantitative policy analysis, the expected value of including uncertainty (EVIU) is the expected difference in the value of a decision based on a probabilistic analysis versus a decision based on an analysis that ignores uncertainty. (Wikipedia).
Expectation Values in Quantum Mechanics
Expectation values in quantum mechanics are an important tool, which help us to mathematically describe measurements of quantum systems. You can think of expectation values as the average of all possible outcomes of a measurement, weighted by their respective probabilities. Contents: 00:
From playlist Quantum Mechanics, Quantum Field Theory
(PP 4.1) Expectation for discrete random variables
(0:00) Definition of expectation for discrete r.v.s. (4:17) Well-defined expectation. (8:15) E(X) may exist and be infinite. (10:58) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
A quick introduction to expected value formulas.
From playlist Basic Statistics (Descriptive Statistics)
Expected Value of the Exponential Distribution | Exponential Random Variables, Probability Theory
What is the expected value of the exponential distribution and how do we find it? In today's video we will prove the expected value of the exponential distribution using the probability density function and the definition of the expected value for a continuous random variable. It's gonna b
From playlist Probability Theory
Expected Value of a Discrete Probability Distribution
This video explains how to determine the expected value or mean value of a discrete probability distribution. http://mathispower4u.com
From playlist Probability
Expected Value of the Bernoulli Distribution | Probability Theory
How do we derive the mean or expected value of a Bernoulli random variable? We'll be going over that in today's probability theory lesson! Remember a Bernoulli random variable is a random variable that is equal to 1 (success) with probability p and equal to 0 (failure) with probability 1-
From playlist Probability Theory
(PP 4.2) Expectation for random variables with densities
(0:00) Definition of expectation for r.v.s. with densities. (2:30) E(X) for a uniform random variable. (5:05) Well-defined expectation. (7:15) E(X) may exist and be infinite. (8:00) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtub
From playlist Probability Theory
(PP 4.4) Properties of expectation
(0:00) Properties of expectation. (6:17) Expectation rule. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
16c Data Analytics: Decision Making
Lecture on decision making in the presence of uncertainty. Follow along with the demonstration workflow in Python: o. Decision making, optimum estimation in the presence of uncertainty: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_DecisionMaking.ipynb Foll
From playlist Data Analytics and Geostatistics
Expected Value Example and Intuitive Explanation
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Expected Value Example and Intuitive Explanation
From playlist Statistics
Data Science Basics: Bootstrap
Live Jupyter walk-through of bootstrap for uncertainty modeling in Python. I demonstrate that we can bootstrap to calculate uncertainty, due to data paucity, for any statistic! This should be enough to get anyone started building data analytics workflows in Python. The demonstrated workfl
From playlist Data Science Basics in Python
20e Spatial Data Analytics: Summarizing Uncertainty
Subsurface modeling course lecture on summarizing uncertainty.
From playlist Spatial Data Analytics and Modeling
Value of Information in the Earth Sciences
Overview, narrated by Tapan Mukerji Eidsvik, J., Mukerji, T. and Bhattacharjya, D., 2015. Value of information in the earth sciences: Integrating spatial modeling and decision analysis. Cambridge University Press.
From playlist Uncertainty Quantification
09b Machine Learning: Linear Regression
Lecture on linear regression as an introduction to machine learning prediction. Includes derivation, prediction and confidence intervals, testing model parameters etc. We start simple and build from these concepts to much more complicated machines! Follow along with the demonstration work
From playlist Machine Learning
With all this talk about "sigma" and certainty at the Large Hadron Collider, we attempt to explain what it's all about. Visit our website at http://www.sixtysymbols.com/ We're on Facebook at http://www.facebook.com/sixtysymbols And Twitter at http://twitter.com/#!/periodicvideos Sixt
From playlist Large Hadron Collider - Sixty Symbols
Reduced form Setting undr Model Uncertainty w/ Nonlinear Affine Intensities - Prof Francesca Biagini
Abstract In this talk we present a market model including financial assets and life insurance liabilities within a reduced-form framework under model uncertainty by following [1]. In particular we extend this framework to include mortality intensities following an affine process unde
From playlist Uncertainty and Risk
Thirteenth SIAM Activity Group on FME Virtual Talk
Speakers: Damir Filipovic, EPFL and Swiss Finance Institute Title: A Machine Learning Approach to Portfolio Pricing and Risk Management for High-Dimensional Problems Moderator: Rene Carmona, Princeton University
From playlist SIAM Activity Group on FME Virtual Talk Series
Statistical Rethinking - Lecture 04
Lecture 04, Linear Models, from Statistical Rethinking, A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Expected Value of a Binomial Probability Distribution
Today, we derive the formula to find the expected value or the mean of a discrete random variable which follows the binomial probability distribution.
From playlist Probability
16b Data Analytics: Model Checking
Spatial, subsurface model checking including statistical inputs, and accuracy of estimates and uncertainty model.
From playlist Data Analytics and Geostatistics