The Most Powerful Tool Based Entirely On Randomness
We see the effects of randomness all around us on a day to day basis. In this video we’ll be discussing a couple of different techniques that scientists use to understand randomness, as well as how we can harness its power. Basically, we'll study the mathematics of randomness. The branch
From playlist Classical Physics by Parth G
Conceptual Questions about Random Variables and Probability Distributions
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Conceptual Questions about Random Variables and Probability Distributions
From playlist Statistics
Randomness Quiz - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Random Matrices and Their Limits - R. Speicher - Workshop 2 - CEB T3 2017
Roland Speicher / 26.10.17 Random Matrices and Their Limits The free probability perspective on random matrices is that the large size limit of random matrices is given by some (usually interesting) operators on Hilbert spaces and corresponding operator algebras. The prototypical example
From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester
(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian
An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.
From playlist Probability Theory
Probability: We define geometric random variables, and find the mean, variance, and moment generating function of such. The key tools are the geometric power series and its derivatives.
From playlist Probability
Lecture on the motivation for simulation vs. estimation and development of the sequential Gaussian simulation approach.
From playlist Data Analytics and Geostatistics
Spatial point data, also known as spatial point patterns, refers to collections of points (or events) in space. Examples include trees in a forest, gold deposits, positions of stars, earthquakes, crime locations, animal sightings, etc. Spatial data analysis, as a statistical exploration of
From playlist Wolfram Technology Conference 2020
Spatial Events-Spatial Statistics
Spatial point patterns are collections of randomly positioned events in space. Examples include trees in a forest, positions of stars, earthquakes, crime locations, animal sightings, etc. Spatial point data analysis, as a statistical exploration of point patterns, aims to answer questions
From playlist Wolfram Technology Conference 2022
Randomness Quiz Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Spatial Events: Spatial Statistics
Spatial point patterns are collections of randomly positioned events in space. Examples include trees in a forest, positions of stars, earthquakes, crime locations, animal sightings, etc. Spatial point data analysis, as a statistical exploration of point patterns, aims to answer questions
From playlist Wolfram Technology Conference 2021
Live CEOing Ep 394: Spatial Statistics Design Review for Wolfram Language
In this episode of Live CEOing, Stephen Wolfram reviews the design of upcoming spatial statistics functionality for the Wolfram Language. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or through the official Twitch channe
From playlist Behind the Scenes in Real-Life Software Design
1b Data Analytics Reboot: Spatial Sampling
Lecture on spatial sampling. Sampling motivation, sampling spatial bias and other biases. Data Analytics and Geostatistics is an undergraduate course that I teach fall and spring semesters at The University of Texas at Austin. We build up fundamental spatial, subsurface, geoscience and en
From playlist Data Analytics and Geostatistics
Suhasini Subba Rao: Fourier based methods for spatial data observed on irregularly spaced locations
Abstract : In this talk we introduce a class of statistics for spatial data that is observed on an irregular set of locations. Our aim is to obtain a unified framework for inference and the statistics we consider include both parametric and nonparametric estimators of the spatial covarianc
From playlist Probability and Statistics
Sudipto Banerjee: High-dimensional Bayesian geostatistics
Abstract: With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarc
From playlist Probability and Statistics
DIRECT 2021 08 Spatial Statistics for Modeling
DIRECT Consortium at The University of Texas at Austin, working on novel methods and workflows in spatial, subsurface data analytics, geostatistics and machine learning. This is Spatial Statistics for Characterization and Modeling Subsurface Fractures by Mahmood Shakiba, supported by th
From playlist DIRECT Consortium, The University of Texas at Austin
Pierre Van Moerbeke: Universality in tiling models
We consider the domino tilings of a large class of Aztec rectangles. For an appropriate scaling limit, we show that, the disordered region consists of roughly two arctic circles connected with a finite number of paths. The statistics of these paths is governed by a kernel, also found in ot
From playlist Probability and Statistics
Dr Holloway-Brown-Stochastic spatial random forest for detecting remotely sensed forest cover change
Dr Jacinta Holloway-Brown (University of Adelaide) presents "Stochastic spatial random forest for detecting remotely sensed forest cover change despite missing data", 14 October 2022.
From playlist Statistics Across Campuses