Modeling and Simulation Silabus

Slides:



Advertisements
Similar presentations
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. A PowerPoint Presentation Package to Accompany Applied Statistics.
Advertisements

Simulation - An Introduction Simulation:- The technique of imitating the behaviour of some situation or system (economic, military, mechanical, etc.) by.
CSE 202 – Formal Languages and Automata Theory 1 REGULAR LANGUAGE.
1 Overview of Simulation When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions set for analytic.
11 Simulation. 22 Overview of Simulation – When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions.
Operations Research 2 Nur Aini Masruroh. Contents Introduction 1 Course outline 2 References 3 Grading 4.
BAYESIAN INFERENCE Sampling techniques
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
Classification of Simulation Models
Discrete-Event Simulation: A First Course Steve Park and Larry Leemis College of William and Mary.
Computational statistics, course introduction Course contents  Monte Carlo Methods  Random number generation  Simulation methodology  Bootstrap  Markov.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
Descriptive Modelling: Simulation “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose.
The Monte Carlo Method: an Introduction Detlev Reiter Research Centre Jülich (FZJ) D Jülich
 1  GSLM System Simulation Yat-wah Wan Room: B317; ywan; Ext: 3166.
+ JAVA Programming CCSA Introduction this course is a hands-on course in programming with the Java language for students who have completed a course.
Computer Simulation A Laboratory to Evaluate “What-if” Questions.
Probability Distributions. A sample space is the set of all possible outcomes in a distribution. Distributions can be discrete or continuous.
Modeling and Simulation
Performance Evaluation of Computer Systems and Networks By Behzad Akbari Tarbiat Modares University Spring 2012 In the Name of the Most High.
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
Chapter 14 Monte Carlo Simulation Introduction Find several parameters Parameter follow the specific probability distribution Generate parameter.
Modeling and Simulation
David Watling, Richard Connors, Agachai Sumalee ITS, University of Leeds Acknowledgement: DfT “New Horizons” Dynamic Traffic Assignment Workshop, Queen’s.
Reliability analysis of statically indeterminate steel frame (pilot study) David Pustka VŠB – Technical University of Ostrava Faculty of Civil Engineering.
ECE 250 Algorithms and Data Structures Douglas Wilhelm Harder, M.Math. LEL Department of Electrical and Computer Engineering University of Waterloo Waterloo,
CDA6530: Performance Models of Computers and Networks Cliff Zou Fall 2013.
Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.
Introduction to Simulation K.Sailaja Kumar 1 SYSTEM SIMULATION AND MODELLING Course Code: MCA 52 Faculty : Sailaja Kumar k.
Markov Chain Monte Carlo for LDA C. Andrieu, N. D. Freitas, and A. Doucet, An Introduction to MCMC for Machine Learning, R. M. Neal, Probabilistic.
Chapter 8 Random-Variate Generation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation.
SOME IMPORTANT AND RELEVANT TEXTS. PRACTICAL STATISTICS R. Walpole and R. Myers – Probability and Statistics for Engineers and Scientists J. Devore –
Modeling and Simulation Dr. X. Topics What is Continuous Simulation Why is it useful? Continuous simulation design.
Teaching Empirical Skills and Concepts in Computer Science using Random Walks Grant Braught Dickinson College
Figure 1. First period harvest units on the Putnam Tract using mixed integer programming methods.
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
CSE 202 – Formal Languages and Automata Theory 1 REGULAR EXPRESSION.
CS/APMA 202 Spring 2005 Aaron Bloomfield. Sequences in Nature
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 15-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Prepared by.
NETW 707: Modeling & Simulation Course Instructor: Tallal Elshabrawy Instructor Office: C3.321 Instructor Teaching.
Variance reduction techniques Mat Simulation
Advanced Software Engineering
Monte Carlo Simulation Random Number Generation
ECE3340 Introduction to Stochastic Processes and Numerical Methods
Course Introduction 공학대학원 데이타베이스
Lecture 0 Software Engineering Course Introduction
Object-Oriented Analysis & Design
Advanced Software Engineering
Prepared by Lloyd R. Jaisingh
Basic simulation methodology
Modeling and Simulation (An Introduction)
PowerPoint Presentation Materials Transportation Engineering
Chapter 1 Introduction to Simulation Modeling
Date of download: 12/16/2017 Copyright © ASME. All rights reserved.
Principles of Computing – UFCFA Lecture-1
Chapter 10 Verification and Validation of Simulation Models
قهرمانی گروه مهندسی کامپیوتر
Prepared by Lee Revere and John Large
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Modeling and Simulation: Fundamentals and Implementation
Simulation and Modeling
Chapter 8 Random-Variate Generation
Course Information Teacher: Cliff Zou Course Webpage:
Random WALK, BROWNIAN MOTION and SDEs
Chapter 14 Monte Carlo Simulation
Principles of Computing – UFCFA Week 1
MECH 3550 : Simulation & Visualization
Š. Emrich, M. Bruckner, S. Zerlauth, S. Tauböck, J. Funovits, N
11. Monte Carlo Applications
Empirical Distributions
Presentation transcript:

Modeling and Simulation Silabus Tri Harsono Politeknik Elektronika Negeri Surabaya (PENS) Materi ajar Modeling and Simulation

Materi ajar Modeling and Simulation Objectives This course will teach: Concepts and techniques of modeling; Emphasis will be on the design and analysis of models: simulation discrete and continuous simulation using a programming language; Random number generation, random variable generation, model verification and validation, applications Materi ajar Modeling and Simulation

Materi ajar Modeling and Simulation Detailed Syllabus Introduction to Modeling and Simulation Random Number Generation Random Variate Generation Simulation using Monte Carlo Random Walk Simulated Annealing Queuing Theory Cellular automata Model Epidemik Materi ajar Modeling and Simulation

Materi ajar Modeling and Simulation References Jerry Banks, John Carson, Barry Nelson, David Nicol, “Discrete Event System Simulation” Averill Law, W. David Kelton, “Simulation Modeling and Analysis”, McGRAW-HILL Materi ajar Modeling and Simulation

Materi ajar Modeling and Simulation Evaluations UTS: 30% UAS: 40% Project: 30% Materi ajar Modeling and Simulation