A dynamic unobserved effects model is a statistical model used in econometrics for panel analysis. It is characterized by the influence of previous values of the dependent variable on its present value, and by the presence of unobservable explanatory variables. The term “dynamic” here means the dependence of the dependent variable on its past history; this is usually used to model the “state dependence” in economics. For instance, for a person who cannot find a job this year, it will be harder to find a job next year because her present lack of a job will be a negative signal for the potential employers. “Unobserved effects” means that one or some of the explanatory variables are unobservable: for example, consumption choice of one flavor of ice cream over another is a function of personal preference, but preference is unobservable. (Wikipedia).
Fuzzy control of inverted pendulum
Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance.
From playlist Demonstrations
Fixed Effects and Random Effects
Brief overview in plain English of the differences between the types of effects. Problems with each model and how to overcome them.
From playlist Experimental Design
Review of Linear Time Invariant Systems
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Review: systems, linear systems, time invariant systems, impulse response and convolution, linear constant-coefficient difference equations
From playlist Introduction and Background
Similar Figures: Effects on Areas
Link: https://www.geogebra.org/m/k6a9hf4h
From playlist Geometry: Dynamic Interactives!
System Identification: Full-State Models with Control
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Nadav Cohen: "Implicit Regularization in Deep Learning: Lessons Learned from Matrix & Tensor Fac..."
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop I: Tensor Methods and their Applications in the Physical and Data Sciences "Implicit Regularization in Deep Learning: Lessons Learned from Matrix and Tensor Factorization" Nadav Cohen - Tel Aviv Unive
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
If Corresponding Angles are Congruent, then...?
Link: https://www.geogebra.org/m/hb3xXZeF
From playlist Geometry: Dynamic Interactives!
Statistical Rethinking 2022 Lecture 16 - Gaussian Processes
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: https://www.youtube.com/watch?v=uYNzqgU7na4 Music: https://www.youtube.com/watch?v=kXuasY8pDpA Music: https://www.youtube.com/watch?v=eTtTB0nZdL0 Pause: https://www.youtube.com/watch?v=pxPdsqrQByM
From playlist Statistical Rethinking 2022
Statistical Rethinking 2023 - 16 - Gaussian Processes
Course: https://github.com/rmcelreath/stat_rethinking_2023 Intro music: https://www.youtube.com/watch?v=_3XGEsDSInM Outline 00:00 Introduction 02:37 Oceanic spatial confounds 09:54 Gaussian processes 24:26 Oceanic Gaussian process 33:51 Pause 34:37 Phylogenetic regression 1:18:39 Summary
From playlist Statistical Rethinking 2023
Inspired by https://www.youtube.com/watch?v=EYkBctqyKic
From playlist Handmade geometric toys
Topics in Dynamical Systems: Fixed Points, Linearization, Invariant Manifolds, Bifurcations & Chaos
This video provides a high-level overview of dynamical systems, which describe the changing world around us. Topics include nonlinear dynamics, linearization at fixed points, eigenvalues and eigenvectors, bifurcations, invariant manifolds, and chaos!! @eigensteve on Twitter eigensteve.co
From playlist Dynamical Systems (with Machine Learning)
Counterfactual Fairness: Matt Kusner, The Alan Turing Institute
Dr Kusner is a Research Fellow at The Alan Turing Institute. He was previously a visiting researcher at Cornell University, under the supervision of Kilian Q Weinberger, and received his PhD in Machine Learning from Washington University in St Louis. His research is in the areas of counter
From playlist AI for Social Good
Statistical Rethinking 2023 - 06 - Good & Bad Controls
Course details: https://github.com/rmcelreath/stat_rethinking_2023 Intro music: https://www.youtube.com/watch?v=PDohhCaNf98 Outline 00:00 Introduction 01:43 Causal implications 14:28 do-calculus 16:59 Backdoor criterion 40:48 Pause 41:22 Good and bad controls 1:09:34 Summary 1:26:27 Bonu
From playlist Statistical Rethinking 2023
Necmiye Ozay: "A fresh look at some classical system identification methods"
Intersections between Control, Learning and Optimization 2020 "A fresh look at some classical system identification methods" Necmiye Ozay - University of Michigan Abstract: System identification has a long history with several well-established methods, in particular for learning linear d
From playlist Intersections between Control, Learning and Optimization 2020
A Conceptual Approach to Controllability and Observability | State Space, Part 3
Check out the other videos in the series: https://youtube.com/playlist?list=PLn8PRpmsu08podBgFw66-IavqU2SqPg_w Part 1 - The state space equations: https://youtu.be/hpeKrMG-WP0 Part 2 - Pole placement: https://youtu.be/FXSpHy8LvmY Part 4 - What Is LQR Optimal Control: https://youtu.be/E_RD
From playlist State Space
Statistical Rethinking 2022 Lecture 10 - Counts & Confounds
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music etc: Intro: https://www.youtube.com/watch?v=Qut2getKFT4 River Kelvin: https://www.youtube.com/watch?v=hh2Vs13sdNk Tide machine: https://www.youtube.com/watch?v=DmxLUb8g10Q Lego tides: https://www.y
From playlist Statistical Rethinking 2022
Statistical Rethinking 2022 Lecture 17 - Measurement Error
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: Music: https://www.youtube.com/watch?v=xXHH6bBAjDQ Palms: https://www.youtube.com/watch?v=We2KHqtqDos Pancake: https://www.youtube.com/watch?v=44ORuxym4fo Pause: https://www.youtube.com/watch?v=p
From playlist Statistical Rethinking 2022
Mateus Juda (7/29/20): Unsupervised features learning for sampled vector fields
Title: Unsupervised features learning for sampled vector fields Abstract: In this talk we introduce a new approach to computing hidden features of sampled vector fields. The basic idea is to convert the vector field data to a graph structure and use tools designed for automatic, unsupervi
From playlist AATRN 2020
The Blessings of Multiple Causes - David M. Blei
Seminar on Theoretical Machine Learning Topic: The Blessings of Multiple Causes Speaker: David M. Blei Affiliation: Columbia University Date: January 21, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Similar Figures Definition: Dynamic Illustration
Link: https://www.geogebra.org/m/EeXdSpJB
From playlist Geometry: Dynamic Interactives!