I'm a second year undergraduate statistic student, I need a good reference to learn these topics
Markov Chains in discrete time.
1.1. Classification of states, recurrence notions of transience.
1.2. Stationary measure.
1.3. Reversibility.
Markov Chains continuous time.
- Poisson process.
- Processes of birth and death.
- Applications: renewal theory notions of queuing theory.
I've had a look and seem to have video classes at MIT, I appreciate any indication
Is there any significant difference between:
- Sheldon M. Ross "Stochastic processes"
- Sheldon M. Ross "Introduction to probability models"
EDIT:I'm sorry for reviving this question, but I've been trying to study through 2. Sheldon M. Ross "Introduction to probability models", but this book is very tiring to read, now I'm looking for lecture notes or a more concise book only for the theory without many examples, and use Ross's book only to solve the exercises, since this has solutions.
Has anyone used any of the books below for reference?
A good starting point would be Introduction to Probability Models by Sheldon Ross. I think it covers all the topics that you described. I like the book because it's easy to read and has plenty of problems to try out, which makes it ideal for self learning.