Monte Carlo methods | Randomized algorithms

Biology Monte Carlo method

Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion transport in an electrolyte environment through ion channels or nano-pores embedded in membranes. It is a 3-D particle-based Monte Carlo simulator for analyzing and studying the ion transport problem in ion channel systems or similar nanopores in wet/biological environments. The system simulated consists of a protein forming an ion channel (or an artificial nanopores like a Carbon Nano Tube, CNT), with a membrane (i.e. lipid bilayer) that separates two ion baths on either side. BioMOCA is based on two methodologies, namely the Boltzmann transport Monte Carlo (BTMC) and particle-particle-particle-mesh (P3M). The first one uses Monte Carlo method to solve the Boltzmann equation, while the later splits the electrostatic forces into short-range and long-range components. (Wikipedia).

Biology Monte Carlo method
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What is the Monte Carlo method? | Monte Carlo Simulation in Finance | Pricing Options

In today's video we learn all about the Monte Carlo Method in Finance. These classes are all based on the book Trading and Pricing Financial Derivatives, available on Amazon at this link. https://amzn.to/2WIoAL0 Check out our website http://www.onfinance.org/ Follow Patrick on twitter h

From playlist Exotic Options & Structured Products

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An introduction to multilevel Monte Carlo methods – Michael Giles – ICM2018

Numerical Analysis and Scientific Computing Invited Lecture 15.7 An introduction to multilevel Monte Carlo methods Michael Giles Abstract: In recent years there has been very substantial growth in stochastic modelling in many application areas, and this has led to much greater use of Mon

From playlist Numerical Analysis and Scientific Computing

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Monte Carlo Integration In Python For Noobs

Monte Carlo is probably one of the more straightforward methods of numerical Integration. It's not optimal if working with single-variable functions, but nonetheless is easy to use, and readily generalizes to multi-variable functions. In this video I motivate the method, then solve a one-d

From playlist Daily Uploads

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Statistics: Ch 4 Probability and Statistics (71 of 74) Monte Carlo Simulation: Ex. 4

Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will use the Monte Carlo simulation to find the number of times it would take to find the correct key to unlock a door. Given: 4 k

From playlist STATISTICS CH 4 STATISTICS IN PROBABILITY

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Statistics: Ch 4 Probability and Statistics (68 of 74) Monte Carlo Simulation: Example 1

Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will use the Monte Carlo simulation to find the most likely duration (in months) it take to complete 5 projects by assigning the l

From playlist STATISTICS CH 4 STATISTICS IN PROBABILITY

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Monte Carlo Simulation and Python

Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 Here we bring at least the initial batch of tutorials to a close with the 3D plotting of our variables in search for preferable settings to use.

From playlist Monte Carlo Simulation with Python

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AQC 2016 - Adiabatic Quantum Computer vs. Diffusion Monte Carlo

A Google TechTalk, June 29, 2016, presented by Stephen Jordan (NIST) ABSTRACT: While adiabatic quantum computation using general Hamiltonians has been proven to be universal for quantum computation, the vast majority of research so far, both experimental and theoretical, focuses on stoquas

From playlist Adiabatic Quantum Computing Conference 2016

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Gunther Leobacher: Quasi Monte Carlo Methods and their Applications

In the first part, we briefly recall the theory of stochastic differential equations (SDEs) and present Maruyama's classical theorem on strong convergence of the Euler-Maruyama method, for which both drift and diffusion coefficient of the SDE need to be Lipschitz continuous. VIRTUAL LECTU

From playlist Virtual Conference

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Research Methods of Biopsychology

With some information regarding the organization of neurons and neural pathways, we are ready to start getting into some deeper topics. But before we do that, it will be useful to get a general sense of precisely how we learn about the things we will be discussing. The brain is complicated

From playlist Biopsychology

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Journey trough statistical physics of constraint satisfaction and inference... by Lenka Zdeborova

26 December 2016 to 07 January 2017 VENUE: Madhava Lecture Hall, ICTS Bangalore Information theory and computational complexity have emerged as central concepts in the study of biological and physical systems, in both the classical and quantum realm. The low-energy landscape of classical

From playlist US-India Advanced Studies Institute: Classical and Quantum Information

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Putting Order into Disorder: An Application to the Chronology of my Works by Giorgio Parisi

DISTINGUISHED LECTURES PUTTING ORDER INTO DISORDER: AN APPLICATION TO THE CHRONOLOGY OF MY WORKS. SPEAKER Giorgio Parisi (Sapienza University, Rome, Italy) DATE : 16 December 2021, 14:00 to 16:00 VENUE: Onine Giorgio Parisi is an Italian theoretical physicist, whose research has foc

From playlist Celebrating the Science of Giorgio Parisi (ONLINE)

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The Power of Sampling by Peter W. Glynn

Infosys-ICTS Turing Lectures The Power of Sampling Speaker: Peter W. Glynn (Stanford University, USA) Date: 14 August 2019, 16:00 to 17:00 Venue: Ramanujan Lecture Hall, ICTS Bangalore Sampling-based methods arise in many statistical, computational, and engineering settings. In engine

From playlist Infosys-ICTS Turing Lectures

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Statistical Rethinking 2022 Lecture 08 - Markov chain Monte Carlo

Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: Intro: https://www.youtube.com/watch?v=E06X1NXRdR4 Skate1 vid: https://www.youtube.com/watch?v=GCr0EO41t8g Skate1 music: https://www.youtube.com/watch?v=o3WvAhOAoCg Skate2 vid: https://www.youtube

From playlist Statistical Rethinking 2022

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Statistical Rethinking 2023 - 08 - Markov Chain Monte Carlo

Course materials: https://github.com/rmcelreath/stat_rethinking_2023 Intro video: https://www.youtube.com/watch?v=Q3jVk6k6CGY Intro music: https://www.youtube.com/watch?v=kNRIFhkYONc Outline 00:00 Introduction 13:08 King Markov 18:14 MCMC 28:00 Hamiltonian Monte Carlo 39:32 Pause 40:06 N

From playlist Statistical Rethinking 2023

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Statistical Rethinking Fall 2017 - week06 lecture10

Week 06, lecture 10 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 8. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http://xcel

From playlist Statistical Rethinking Fall 2017

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Introduction to Chemistry Laboratory Techniques

We've learned a lot of chemistry together, but now it's time to jump into the lab and put it to use! What are some common techniques that every chemistry student and budding chemist must know? Let's go through these step-by-step, with real demonstrations, so that you can nail the lab compo

From playlist Chemistry Laboratory Techniques

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Anthony Maggs: Irreversible Monte Carlo methods for particle simulations

HYBRID EVENT Recorded during the meeting "On Future Synergies for Stochastic and Learning Algorithms" the September 28, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide m

From playlist Probability and Statistics

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