Random self-similar trees: dynamical pruning and its applications to inviscid Burgers equations

Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=VeWp2vAjerQ



Duration: 47:29
818 views
6


Consider the fractional Brownian motions on the real line. What should we expect if we replace the real line by a manifold M? We will provide an answer to this question, extending work begun by Paul Levy in 1965. We will construct a family of Gaussian processes indexed by M with certain properties and argue that these objects are the proper generalization of fractional Brownian motion to the setting of GRF-s over a manifold. We also construct analogs for the Ornstein-Uhlenbeck process indexed by M. After discussing existence, invariance, self-similarity, regularity and Hausdorff dimension, we will give some examples for different manifolds, discuss simulation, and suggest some open problems for future work. 

See more on this video at https://www.microsoft.com/en-us/research/video/pacific-northwest-probability-seminar-random-self-similar-trees-dynamical-pruning-and-its-applications-to-inviscid-burgers-equations/




Other Videos By Microsoft Research


2017-12-11Foundations of Data Science - Lecture 5 - Length Squared Sampling in Matrices
2017-12-11Detecting and Recognizing Text in Natural Images
2017-12-11CodeTalk: Rethinking IDE Accessibility
2017-12-11Seattle Angel Conference XII: Pitch 4-6
2017-12-11Seattle Angel Conference XII: Pitch Overview 1-3
2017-12-10Seattle Angel Conference XII: Announcements, Alumni Reports, and Keynote
2017-12-10Universal Fault-Tolerant Computing with Bacon-Shor Codes
2017-12-10Vega-Lite: A Grammar of Interactive Graphics
2017-12-06Disparity | Artist in Residence
2017-12-05Building a New View of Transcriptome Variations
2017-12-04Random self-similar trees: dynamical pruning and its applications to inviscid Burgers equations
2017-12-04Explaining Inconclusive Outcomes from Software Model Checkers to Users
2017-11-27Pacific Northwest Probability Seminar: An Analysis of Spatial Mixing
2017-11-20Keynote: Smart Enough to Work With Us? Foundations and Challenges for Teamwork-Enabled AI Systems
2017-11-19Pacific Northwest Probability Seminar: Gravitational Allocation to Uniform Points on the Sphere
2017-11-19Pacific Northwest Probability Seminar: A Characterization Theorem for the Gaussian Free Field
2017-11-19Two-round Secure Multiparty Computations from Minimal Assumptions
2017-11-19Intent and Emotions in Image Search and Viewing
2017-11-19Using Large Scale Genomic Databases to Improve Disease Variant Interpretation
2017-11-19Pacific Northwest Probability Seminar: Optimal Matching of Gaussian Samples
2017-11-16Foundations of Data Science - Lecture 4



Tags:
microsoft research