Empirical modelling

Molecular and Empirical Formulas

Introduction to molecular and empirical formulas. Calculating molecular mass. More free lessons at: http://www.khanacademy.org/video?v=gfBcM3uvWfs

From playlist Chemistry

What is the difference between theoretical and experimental physics?

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From playlist Science Unplugged: Physics

Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka

***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th

From playlist Data Warehousing Tutorial Videos

Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Simulation: The Challenge for Data Science

While machine learning has recently had dramatic successes, there is a large class of problems that it will never be able to address on its own. To test a policy proposal, for example, often requires understanding a counterfactual scenario that has never existed in the past, and that may

From playlist Turing Seminars

Eric Vanden-Eijnden (DDMCS@Turing): Neural networks as interacting particle systems

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Sebastian Reich (DDMCS@Turing): Learning models by making them interact

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Statistical Modeling in Business Analytics with R | Edureka

( R Training : https://www.edureka.co/r-for-analytics ) A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more other variables. The topics in

From playlist R Tutorial Videos

Vicky Fasen-Hartmann: Empirical spectral processes for stationary state space models

In this talk, we consider function-indexed normalized weighted integrated periodograms for equidistantly sampled multivariate continuous-time state space models which are multivariate continuous-time ARMA processes. Thereby, the sampling distance is fixed and the driving Lévy process has a

From playlist Probability and Statistics

Stanford CS229: Machine Learning | Summer 2019 | Lecture 13-Statistical Learning Uniform Convergence

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3py8nGr Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html

From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 - Fast Reinforcement Learning III

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu

From playlist Stanford CS234: Reinforcement Learning | Winter 2019

Structured Regularization Summer School - A. Montanari - 4/4 - 22/06/2017

Andrea Montanari (Stanford): Matrix and graph estimation Abstract: Many statistics and unsupervised learning problems can be formalized as estimating a structured matrix or a graph from noisy or incomplete observations. These problems present a large variety of challenges, and an intrigu

From playlist Structured Regularization Summer School - 19-22/06/2017

Emilie Kaufmann - Optimal Best Arm Identification with Fixed Confidence

This talk proposes a complete characterization of the complexity of best-arm identification in one-parameter bandit models. We first give a new, tight lower bound on the sample complexity, that is the total number of draws of the arms needed in order to identify the arm with

From playlist Schlumberger workshop - Computational and statistical trade-offs in learning

Statistical Modeling in Business Analytics with R | Edureka

( R Training : https://www.edureka.co/r-for-analytics ) A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more other variables. The topic co

From playlist R Tutorial Videos

Risk Management Lesson 8A: Industrial Models for Credit Risk

In this first part of Lesson 8, we deal with two important credit risk models developed by the industry. Topics: - Moody's KMV - CreditMetrics (J.P. Morgan & Co.)

From playlist Risk Management

Prospect Theory and Stock Market Anomalies - L. Jin - 1/31/2020

"Prospect Theory and Stock Market Anomalies" Lawrence Jin, Assistant Professor of Finance, Caltech Abstract: This talk discusses some recent development in the field of behavioral finance, with a focus on a new model of asset prices in which investors evaluate risk according to prospect t

From playlist HSS Caltech + Finance 2020

Brief Introduction to Probability and Simulation: Part 3 - Elaine Spiller

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod

From playlist Data Assimilation Research Program

Jean-Michel Zakoïan: Testing the existence of moments for GARCH-type processes

It is generally admitted that financial time series have heavy tailed marginal distributions. When time series models are fitted on such data, the non-existence of appropriate moments may invalidate standard statistical tools used for inference. Moreover, the existence of moments can be cr

From playlist Probability and Statistics

God, Science, and Epistemology - A Conversation with Quentin Lee (Theism vs. Atheism)

Quentin Lee and I talk about epistemology, philosophy of science, and theology. To get in touch: Email: mathoma1517@gmail.com Twitter: @Math_oma Stuff mentioned in discussion: 1. "Five Proofs of the Existence of God" by Edward Feser: https://www.amazon.com/Five-Proofs-Existence-Edward-F

From playlist Conversations

Fourier Series: Modeling Nature

An intuitive means of understanding the power of Fourier series in modeling nature, to place Fourier series in a physical context for students being introduced to the material. A non-technical, qualitative exploration into applications of Fourier Series. 0:17 Ancient Greek theory of celes

From playlist Data Science