Please do the required reading before attempting problems. Not only does it contain the material you need for the week’s work, and more examples than in the lectures, it will remind you of the details that you have to keep in mind when attempting problems. You will solve problems faster if you read first.
The course follows the textbook in sequence. So you will always know which chapters are going to be covered: start where you left off in the previous week, and compare the home page calendar with the Table of Contents to see where to stop.
Lectures will cover Chapters 9 and 10 this week.
Highly recommended: Pitman Section 6.2.
The goal of practice is for you to figure out how to get started on a solution and see it through to the end. To achieve this, you have to be prepared to go over the background reading and examples and then mess around with the ideas yourself to come up with steps that will lead to a solution. It will not be achieved if you just read solutions created by others.
Each Review Set has two parts: The Basics and Additional Practice. Please do all the problems on the Basics list below. Then do as many as you can from the Additional Practice list. You might not have time to get to them all. That’s OK – if you have been practising coming up with solutions yourself, you should be able to tackle new problems on tests.
The Review Set on Conditioning and Markov Chains is based on the material of Chapters 9 through 11.
It’s very short. Please do all the problems not discussed in section.
In section you will work on practice problems and also on the week’s lab if needed. The proportion of time spent on the two activities will depend on the relative demands of the lab and the week’s theory.
The focus will be on approaches to problem-solving. So for example you might develop clear outlines for how to solve several problems, instead of finding the detailed answers to just a few.
Sections will go over Parts 1 and 4 of Lab 4, but some of the calculations might be left for you to complete.
In addition, sections will cover Review Set on Conditioning and Markov Chains 3, 4, 6, and Konstantopoulos 21, 27.