慶應義塾大学　経済学部　PEARL入試　志望理由書　提出例(中妻 照雄先生ゼミ向け)

慶應義塾大学　経済学部　PEARL入試　志望理由書　提出例(中妻 照雄先生ゼミ向け)

Dr. Teruo Nagatsuma

Professor

Department of Economics, Econometrics

Keio University

Dear Professor Nagatsuma,

I am writing this letter to explain my purpose in applying for Department of Economics at Keio University, specializing in Econometrics and related studies. The more data we have, the need for processing them only get bigger. I have read a number of your published work which I was very intrigued by. I hope I am able to elaborate on area of studies that can be a research subject in your seminar, and I would be more than grateful if you could kindly give this a consideration.

Abstract
Bayesian analysis, is a method of statistical inference named after mathmatician that allows one to combine prior information about a population parameter with evidence contained in a sample to guide the statistical inference process. Meanwhile, Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. We often hear peolple question, what is the relationship between bayesian statistics and machine learning? I think the simple answer is that, machine learning is a broad field that uses statistical models and algorithms to automatically learn about a system, typically with the purpose of making predictions about that system in the future.

Question
What is the significance of  bayesian models in Data Science?

Findings
Bayesian statistics encompasses a specific class of models that could be used for Data Science. It can be useful for a variety of reasons such as, having relatively few data points, having strong prior intuitions, having high levels of uncertainty. This is why Bayesian statistics simply is one branch of statistics that deliver well in the field of data science where big data and predictive analytics have become so prominent. For example, based on some knowledge, we can draw some initial set of inferences regarding the system and then “update” these inferences based on data. As we can see clearly, we can constantly make data-driven inferences about the system and keep updating them as more and more data becomes available. Since Bayesian statistics provides a framework for updating “knowledge”, it is in fact used a whole lot in machine learning. 

Summary
There are scenarios where other statistics will perform drastically. More than ever, the importance of understanding theories around data and econometrics has been of focus. I think this is a very important topic with room for further review and I would love to take part in your seminar to conduct more research. Thank you very much for taking the time and I look forward to hearing from you soon.

Sincerely Yours,

*Bayesian Methods for Data Analysis, Book by Bradley Carlin and Thomas A. Louis, 2007