IE 332 • Midterm • Mathematical Modeling and Optimization III
Eğitmen
Ömer Faruk Altun
Co-founder & Head of Education
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 13 yıldır Altun ismiyle başta Sabancı Üniversitesi olmak üzere çeşitli okullarda Endüstri ve Bilgisayar Mühendisliği alanlarında ders vermekteyim. Unicourse'ta sunduğum derslerin yanında eğitim departmanının da sorumluluğunu üstlenmekteyim.
Paketi Tamamla
🎓 TED Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
Konular
Probability Review (İzlemeden Başlama)
Random Variables and Probability Distributions
Conditional Probability
Total Probability Rule
Expected Value
Discrete Time Markov Chains
What is a Markov Chain?
One-Step Transition Probabilities
Example
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Example
Unconditional State Probabilities
Example
Steady-State Distribution and Classification of States
Steady State Distribution
Example
Classes and State Properties
Periodicity and Ergodic Markov Chains
Example
Example
Example
Long Term Properties of Markov Chains
First Passage Times
Example
Expected First Passage Time
Expected Recurrence Time
Example
Absorbing Markov Chains
Absorbing Markov Chains
Matrix Structure
Inverse of a Matrix
Expected Number of State Visits
Example
Expected Time Until Absoption
Example
Absorption Probabilities
Example
Poisson Processes
Counting Processes
Assumptions of Poisson Processes
Example 1
Memoryless Property
Example 2
Minimum of Exponential Random Variables
Example 3
Thinning
Example 4
Superposition
Example 5
Sample Midterm Problems
Discrete Time Markov Chains 1
Discrete Time Markov Chains 2
Discrete Time Markov Chains 3
Discrete Time Markov Chains 4
Limiting Distribution and State Classifications 1
Limiting Distribution and State Classifications 2
Limiting Distribution and State Classifications 3
Limiting Distribution and State Classifications 4
Limiting Distribution and State Classifications 5
Limiting Distribution and State Classifications 6
Limiting Distribution and State Classifications 7
Long Term Properties of Markov Chains 1
Long Term Properties of Markov Chains 2
Absorbing Markov Chains 1
Absorbing Markov Chains 2
Poisson Processes 1
Poisson Processes 2
Poisson Processes 3
Poisson Processes 4
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Probability Review (İzlemeden Başlama)
Random Variables and Probability Distributions
Conditional Probability
Total Probability Rule
Expected Value
Discrete Time Markov Chains
What is a Markov Chain?
One-Step Transition Probabilities
Example
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Example
Unconditional State Probabilities
Example
Steady-State Distribution and Classification of States
Steady State Distribution
Example
Classes and State Properties
Periodicity and Ergodic Markov Chains
Example
Example
Example
Long Term Properties of Markov Chains
First Passage Times
Example
Expected First Passage Time
Expected Recurrence Time
Example
Absorbing Markov Chains
Absorbing Markov Chains
Matrix Structure
Inverse of a Matrix
Expected Number of State Visits
Example
Expected Time Until Absoption
Example
Absorption Probabilities
Example
Poisson Processes
Counting Processes
Assumptions of Poisson Processes
Example 1
Memoryless Property
Example 2
Minimum of Exponential Random Variables
Example 3
Thinning
Example 4
Superposition
Example 5
Sample Midterm Problems
Discrete Time Markov Chains 1
Discrete Time Markov Chains 2
Discrete Time Markov Chains 3
Discrete Time Markov Chains 4
Limiting Distribution and State Classifications 1
Limiting Distribution and State Classifications 2
Limiting Distribution and State Classifications 3
Limiting Distribution and State Classifications 4
Limiting Distribution and State Classifications 5
Limiting Distribution and State Classifications 6
Limiting Distribution and State Classifications 7
Long Term Properties of Markov Chains 1
Long Term Properties of Markov Chains 2
Absorbing Markov Chains 1
Absorbing Markov Chains 2
Poisson Processes 1
Poisson Processes 2
Poisson Processes 3
Poisson Processes 4
Sıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.