Graphical models | Markov models
A dependability state diagram is a method for modelling a system as a Markov chain. It is used in reliability engineering for availability and reliability analysis. It consists of creating a finite state machine which represent the differentstates a system may be in. Transitions between states happen as a result of events from underlying Poisson processes with different intensities. (Wikipedia).
The dependent product as universal construction
In this video I elaborate on the general arrow theoretic characterization of dependent product (or the dependent product functor) that exists in a Cartesian closed category. This is the dependent product that gives dependent product types its name, and it arises in concrete cases in geomet
From playlist Logic
Eigenvalues and Modes of Linear Systems
In this video we discuss how the eigenvalues of the A matrix lead to the modes of a linear state space system. We will also examine how to chose initial conditions to excite a specific mode. In other words, we use a carefully chosen initial condition to ensure that the state response of
From playlist Control Theory
State Space Models, Part 1: Creation and Analysis
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Create and analyze state-space models using MATLAB® and Control System Toolbox™. State-space models are commonly used for representing linear time-invariant (LTI) systems.
From playlist Control System Design and Analysis
Stateflow Overview (Previous Version: R2013a )
Design and simulate state charts using Stateflow. For an updated version of this video, visit: https://youtu.be/TuL8cFqDu6A
From playlist Event-Based Modeling
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
Everything You Need to Know About Control Theory
Control theory is a mathematical framework that gives us the tools to develop autonomous systems. Walk through all the different aspects of control theory that you need to know. Some of the concepts that are covered include: - The difference between open-loop and closed-loop control - How
From playlist Control Systems in Practice
[Webinar] AI Model Governance in a High Compliance Industry
See more at www.johnsnowlabs.com Model governance defines a collection of best practices for data science – versioning, reproducibility, experiment tracking, automated CI/CD, and others. Within a high-compliance setting where the data used for training or inference contains private health
From playlist AI & NLP Webinars
Using State Machines, Part 1: Supervisory Control
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn the basics of state machines in this MATLAB® Tech Talk by Will Campbell. Watch other videos in this series here: https://bit.ly/3hjmRmu Learn how to use finite sta
From playlist Using State Machines
The Step Response | Control Systems in Practice
Check out the other videos in this series: https://www.youtube.com/playlist?list=PLn8PRpmsu08pFBqgd_6Bi7msgkWFKL33b This video covers a few interesting things about the step response. We’ll look at what a step response is and some of the ways it can be used to specify design requirements f
From playlist Control Systems in Practice
Some Theoretical Results on Model-Based Reinforcement Learning by Mengdi Wang
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE & TIME 04 January 2021 to
From playlist Advances in Applied Probability II (Online)
Reverse mathematical methods for reconstructing molecular dynamics... - 18 October 2018
http://crm.sns.it/event/425/ Reverse mathematical methods for reconstructing molecular dynamics in single cell The latest developments in sequencing and high resolution imaging have led to a recent surge of datasets, requiring new mathematical and statistical methods to analyze the biolog
From playlist Centro di Ricerca Matematica Ennio De Giorgi
Metapopulation dynamics and heterogeneity by Somdatta Sinha
Dynamics of Complex Systems - 2017 DATES: 10 May 2017 to 08 July 2017 VENUE: Madhava Lecture Hall, ICTS Bangalore This Summer Program on Dynamics of Complex Systems is second in the series. The theme for the program this year is Mathematical Biology. Over the past decades, the focus o
From playlist Dynamics of Complex Systems - 2017
Nicolas Torres - A multiple time renewal equation for neural assemblies with elapsed time model
---------------------------------- Institut Henri Poincaré, 11 rue Pierre et Marie Curie, 75005 PARIS http://www.ihp.fr/ Rejoingez les réseaux sociaux de l'IHP pour être au courant de nos actualités : - Facebook : https://www.facebook.com/InstitutHenriPoincare/ - Twitter : https://twitter
From playlist Workshop "Workshop on Mathematical Modeling and Statistical Analysis in Neuroscience" - January 31st - February 4th, 2022
Pilar Hernandez & Stefania Bordoni: Neutrinos Lecture 4/4 ⎮ CERN
Neutrinos remain enigmatic and elusive particles. They are invaluable astronomical and terrestrial messengers that have provided the first hints of physics beyond the standard model. Despite being the second most abundant particles in the universe, we still know little about them and futur
From playlist CERN Academic Lectures
Tenth SIAM Activity Group on FME Virtual Talk
Speaker: Rene Carmona, Paul M. Wythes '55 Professor of Engineering and Finance, ORFE & PACM, Princeton University, Title: Contract theory and mean field games to inform epidemic models. Abstract: After a short introduction to contract theory, we review recent results on models involving
From playlist SIAM Activity Group on FME Virtual Talk Series
Recurrent Neural Networks and Models of Computation - Edward Grefenstette, DeepMind
This talk presents an analysis of various recurrent neural network architectures in terms of traditional models of computation. It makes the case for simpler recurrent architectures being closer to finite state automata, and argues that memory-enhanced architectures support better algorith
From playlist Logic and learning workshop
Reinforcement Learning 7: Planning and Models
Hado Van Hasselt, Research Scientist, discusses planning and models as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
Stochastic RNNs without Teacher-Forcing
We present a stochastic non-autoregressive RNN that does not require teacher-forcing for training. The content is based on our 2018 NeurIPS paper: Deep State Space Models for Unconditional Word Generation https://arxiv.org/abs/1806.04550
From playlist Deep Learning Architectures
Pressure in non-equilibrium (active) systems by Yariv Kafri
PROGRAM URL : http://www.icts.res.in/program/NESP2015 DATES : Monday 26 Oct, 2015 - Friday 20 Nov, 2015 VENUE : Ramanujan Lecture Hall, ICTS Bangalore DESCRIPTION : This program will be organized as an advanced discussion workshop on some topical issues in nonequilibrium statstical phys
From playlist Non-equilibrium statistical physics
