IE 301 • Midterm • Operations Research II
Her ne kadar Operations Research I dersinin devamı gibi dursa da aslında hiç ortak yönü olmayan bu derste yeni kaslar geliştiriyor, olasılıkla modellemeyi öğreniyoruz.
Tabii ki bu derste de adres Unicourse!
Ayda 599 TL, peşin fiyatına 3 taksit
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.
Geçme Garantisi
Derslerimize güveniyoruz. Olur da sınavlarına bizimle hazırlandığın halde dersten kalırsan, iade alabilirsin. Koşullar
Konular
Probability Review
9 konu anlatımı
Counting and Probability
Conditional Probability and Independence
Bayes' Rule
Discrete Random Variables
Continuous Random Variables
Expected Value and Variance
Bernoulli and Binomial Distributions
Continuous Uniform Distribution
Exponential Distribution
Markov Chains
8 konu anlatımı
What is a Markov Chain?
One-Step Transition Probabilities
Example
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Example
Unconditional State Probabilities
Example
🦄 🦄 EXAM PRACTICE - PART 1 🦄 🦄
17 soru
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Probability Review
Markov Chains
Markov Chains
Markov Chains
Markov Chains
Markov Chains
Markov Chains
Markov Chains
Steady State Probabilities and Applications
12 konu anlatımı
Steady State Distribution
Example
Classes and State Properties
Periodicity and Ergodic Markov Chains
Example
Example
Example
First Passage Times
Example
Expected First Passage Time
Expected Recurrence Time
Example
Absorbing Markov Chains
9 konu anlatımı
Absorbing Markov Chains
Matrix Structure
Inverse of a Matrix
Expected Number of State Visits
Example
Expected Time Until Absoption
Example
Absorption Probabilities
Example
🦄 🦄 EXAM PRACTICE - PART 2 🦄 🦄
20 soru
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Steady State Probabilities and Applications
Absorbing Markov Chains
Absorbing Markov Chains
Absorbing Markov Chains
Absorbing Markov Chains
Absorbing Markov Chains
Absorbing Markov Chains
Exponential Distribution and Poisson Processes
11 konu anlatımı
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
🦄 🦄 EXAM PRACTICE - PART 3 🦄 🦄
11 soru
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
Exponential Distribution and Poisson Processes
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