Udemy - Introduction to Monte Carlo Methods

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[ DevCourseWeb.com ] Udemy - Introduction to Monte Carlo Methods
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1 -0_Set_up.html (1.2 MB)
    • 1 -Setting up.en_US.vtt (7.8 KB)
    • 1 -Setting up.mp4 (24.4 MB)
    • 2 -Setting up Jupyter Notebooks.en_US.vtt (3.6 KB)
    • 2 -Setting up Jupyter Notebooks.mp4 (12.6 MB)
    • 3 -Review.en_US.vtt (6.5 KB)
    • 3 -Review.html (1.1 MB)
    • 3 -Review.mp4 (13.3 MB)
    • 3 -Table of Common Distributions.pdf (64.9 KB)
    • 4 -1_Introduction.html (1.2 MB)
    • 4 -Introduction to Monte Carlo Methods.en_US.vtt (13.6 KB)
    • 4 -Introduction to Monte Carlo Methods.mp4 (32.4 MB)
    • 4 -Introduction_ Monte Carlo Methods.pdf (189.5 KB)
    • 5 - Introduction to Monte Carlo Simulation.html (0.5 KB)
    • 5 -nihms219206.pdf (778.4 KB)
      • UdemyMCMC Bootstrap
      • 4 Intro to Bootstrap-script.R (3.4 KB)
      • 4 Intro to Bootstrap.Rmd (7.4 KB)
      • 4_Intro_to_Bootstrap.html (617.1 KB)
        • notebook
        • 4 Bootstrap.ipynb (114.8 KB)
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          • prereqs.cpython-36.pyc (3.1 KB)
          ipynb_checkpoints
          • 4 Bootstrap-checkpoint.ipynb (108.1 KB)
        • mtcars.csv (1.7 KB)
          • Moduel 1
          • 1 Introduction-script.R (2.2 KB)
          • 1 Introduction.Rmd (11.3 KB)
          • 1_Introduction.html (1.2 MB)
            • notebook
            • 1 Introduction.ipynb (39.5 KB)
              • ipynb_checkpoints
              • 1 Introduction-checkpoint.ipynb (39.5 KB)
              Moduel 2
              • 2 Generating Random Variables-script.R (8.3 KB)
              • 2 Generating Random Variables.Rmd (22.9 KB)
              • 2_Generating_Random_Variables.html (1.8 MB)
                • notebook
                • 2 Generating Random Variables.ipynb (408.7 KB)
                Moduel 3
                • 3 Monte Carlo Integration-script.R (6.2 KB)
                • 3 Monte Carlo Integration.Rmd (29.6 KB)
                • 3_Monte_Carlo_Integration.html (1.7 MB)
                  • notebook
                  • 3 Monte Carlo Integration.ipynb (351.2 KB)
                  Moduel 4
                  • 4 Controlling and Accelerating Convergence-script.R (3.4 KB)
                  • 4 Controlling and Monitoring Convergence.Rmd (9.2 KB)
                  • 4_Controlling_and_Monitoring_Convergence.html (1.5 MB)
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                    • 4 Controlling and Accelerating Convergence.ipynb (126.3 KB)
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                      • 4 Controlling and Accelerating Convergence-checkpoint.ipynb (95.7 KB)
                      Moduel 5
                      • 5 MC EM Algorithm.Rmd (15.1 KB)
                      • 5 MC EM-script.R (2.7 KB)
                      • 5_MC_EM_Algorithm.html (493.3 KB)
                        • notebook
                        • 5 Monte Carlo EM.ipynb (60.7 KB)
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                          • 5 Monte Carlo EM-checkpoint.ipynb (60.7 KB)
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                          • Moduel 6
                          • 6 Intro to Markov Chains.Rmd (10.5 KB)
                          • 6 Metropolis-Hastings Algorithms.Rmd (20.4 KB)
                          • 6 Metropolis-Hastings-script.R (11.9 KB)
                          • 6_Intro_to_Markov_Chains.html (1.2 MB)
                          • 6_Metropolis-Hastings_Algorithms.html (2.3 MB)
                            • notebook
                            • 6 Metropolis Hastings Algorithm.ipynb (804.7 KB)
                              • ipynb_checkpoints
                              • 6 Metropolis Hastings Algorithm-checkpoint.ipynb (636.5 KB)
                              Moduel 7
                              • 7 Gibbs Sampler-script.R (4.6 KB)
                              • 7 Gibbs Samplers.Rmd (15.1 KB)
                              • 7_Gibbs_Samplers.html (663.0 KB)
                                • notebook
                                • 7 Gibbs Samplers.ipynb (169.5 KB)
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                                  • 7 Gibbs Samplers-checkpoint.ipynb (169.5 KB)
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Description

Introduction to Monte Carlo Methods

https://DevCourseWeb.com

Last updated 7/2018
Created by Jonathan Navarrete
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 31 Lectures ( 4h 45m ) | Size: 1.15 GB

Statistical Computation, MCMC and Bayesian Statistics

What you'll learn
Apply MCMC to Statistical Modeling
Greater understanding of statistical methods for simulation
How to write code in R or Python
How to perform nonparametric bootstrap
Apply optimization techniques to solve numerical and combinatorial problems
At the end of this course you will learn how to apply Monte Carlo methods to Bayesian problems for data analysis
Build genetic algorithms

Requirements
You should have some experience with R or Python
This course is ideally meant for students in a graduate degree program (i.e. math, statistics, electrical engineering)
If you don't have a solid background with statistics, you should at least be willing to learn
You should have a basic understanding of mathematical statistics and desire to apply Monte Carlo methods



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Udemy - Introduction to Monte Carlo Methods

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Download torrent
1.2 GB
seeders:9
leechers:15
Udemy - Introduction to Monte Carlo Methods

Torrent hash: 54B5AC104520802205B9965D1BADD700006E09F2