Project I(2018): Bayesian Inference: One-parameter Models
Students: Israa F.M. Alhamarna
Ragad W.A. Albarghash
There are two main opposing schools of statistical reasoning, frequentist and Bayesian approaches. Until recent days, the frequentist or classical approach has dominated the scientific research, but Bayesianism has reappeared with a strong impulse that is starting to change the situation. The basic notions about these two approaches to inference are presented and the corresponding terminology is introduced.
Project II(2018): Bayesian Inference: Multi-parameter Models
Student: Maitha khalid AlAli
Two main methods of statistical inference can be found in the literature of statistics. The first one is known as the frequentist (or classical) approach. This approach has been studied in 1440-361 (Mathematical Statistics). The second method to inference, which will be the choice of this project, is called the Bayesian approach. This approach can be viewed as the modern statistics to inference and it differs from the classical one in that it specifies a probability distribution for the parameter(s) of interest. The goal of this project is to extend the one-parameter Bayesian approach to the Multi-parameter case. Examples and real life applications are also considered. Section 1 - Section 6 are done jointly with Israa Alhamarna and Ragad Albarghash.