1. CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference

2. Introduction and motivation  Practical application of probability models:  Observations:  Population:  Sample:

3. Introduction and motivation  What is inference?  Representativeness of the sample:

4. Types of inference problems  Parameter estimation:  Determination of the distribution:  Hypothesis testing:

5. Parameter estimation  Maximum likelihood:

6. Parameter estimation: Bernoulli trials  Parameters to be estimated:  Observations:  Likelihood function:

7. Parameter estimation: Bernoulli trials  Maximum likelihood estimate:  Example:

8. Parameter estimation: Binomial distribution  Parameter to be estimated:  Observations:

9. Parameter estimation: Binomial distribution (contd..)  Likelihood function:

10. Parameter estimation: Binomial distribution (contd..)  Maximum likelihood estimate:

11. Parameter estimation: Binomial distribution (contd..)  Example:

12. Irreducible chains  Example:  Definition:  Steady-state or equilibrium state:

13. Irreducible chains (contd..)  Computation of steady-state probabilities:

14. Irreducible chains (contd..)  Example: