## Course Blog Fall 2016

## Day 8. Probability, Analysis, and Weak Laws

The warm-up problems will deal with the Laplace transform and the MGF. This is both to set up the work with transforms in Chapter 3 and to remind everyone about some basic calculus (e.g. the Gamma function, differentiation under the integral etc.)

There are then two main tasks. First we'll see how Chebyshev's inequality can be used to prove some theorems in analysis such as the Weierstrass approximation theorem and a version of the Laplace inversion formula. We'll then look at some "minor" problems in probability such as records and runs. These are nice to know about even if they do not constitute core theory.

We will shortly need some information about the integrals --- especially integrals related to sin(x)/x. We won't need a ton of information, but this topic offers a beautiful window on classical mathematics. You should take a peek if you have the time. I've written a leisurely scholium on sin(x)/x. It has has more than we need --- but you may still find it useful (even entertaining). Among other things, it gets the Laplace transform of sin (x) by five different methods.

## Day 7. An L^1 Maximal Inequality and a Proof of the SLLN

We'll have a couple of warm-up problems that use the moment generating function. One of these will be used later to give us another version of Hoeffding's Lemma. I also have a symmetry story about the variance.

We then introduce another maximal inequality, the L^1 maximal inequality. This has notable benefits over Kolmogorov's L^2 maximal inequality that can be used to give a very direct proof and very easy proof of the SLLN. If you want to read about this ahead of time, you can check out a brief piece that I did
for the *American Mathematical Monthly*.

As time permits, we'll consider some applications of technology of the ** weak law of large numbers** such as Bernstein's proof of Weierstrass approximation. We also have the
Chung-Erdos lower bound on our to-do list.

HW 5 will be due on Monday, October 3.

## HW 4 Note --- A Complete Graph of Inferences

In problem 4 of HW4, you will want to avoid use of the DCT if you want the benefit of a new proof of the DCT in problem 5. Here you can use the MCT, but perhaps you can even avoid the used of the MCT. Part of the point here is that you can essentially form a complete graph of the implications between the MCT, DCT, and Fatou. Any given book chooses an order, and starting with the MCT is most natural. Still, all 6 of the possible permutations can be done.

## Day 6. Adult Strength SLLN

We'll do two warm-up problems that should put firmly in mind a couple of ideas that we need later in the class. We then prove the SLLN under the assumption of IID random variables and *just a finite first moment.*

This takes a sustained argument with three slices:

**A DCT slice**--- pretty trivial but requiring some knowledge.**A BC lemma slice**--- again easy, but requiring a knowledge of a calculation and a lemma.- A
**Kolmogorov One Series**Slice. Here we use V1.1, and we have to do a nice calculation to make it tick. The warm-up problems help us here.

This argument deserves to be mastered, and we'll take our time with it. If we do have time to spare, we'll prove Feller's Weak Law of Large Numbers. It illustrates the idea that one can make some progress even without a first moment.

## Day 5. Series Theorems of Kolmogorov and Levy

We begin with a warm-up problem or two: The proof of Jensen's inequality is one of these.

We then put the Cauchy criterion into the language of random variables. In particular, we put the difference between convergence in probability and convergence with probability one into a tidy analytical box. Everyone needs to sort out the ways to express what it means for a sequence to be almost surely Cauchy, and everyone needs to see how this differs from the* definition *of a sequence being Cauchy in probability.

We recall Kolmogorov's maximal inequality and how we used it to prove Kolmogorov's One Series Theorem v1.0 and we note that the same argument gives us v1.1. We'll also review how we used the Kronecker lemma to get the SLLN for IID random variables with a finite variance.

We'll then prove a curiously general maximal inequality due to Paul Levy, and we'll use this inequality to prove Levy's Series Theorem which says that for series of independent summands convergence in probability and convergence with probability one are equivalent.

Homework 4 is due Monday September 26 --- the night of the first Presidential debate.

Note: Some people did not do well on HW2 because they did not have deep enough experience with delta-epsilon proofs. This is not a deficit that one can make up with a little extra work; most people need a solid one-year course in analysis to succeed in 930. The demands on your analysis skill will start piling up very substantially, so, if you are not confident in your analysis skill, you should consider switching to auditor status.

## Coaching Added

I added a tiny bit of Cauchy coaching to the end of the HW3 assignment. It may keep you from getting confused in one of the problems.

## Day 4. More Techniques for the SLLN

We begin with three warm-up problems. They are motivated by what they suggest about problem solving. They also give us some facts that we'll need later.

We then give a proof of the SLLN for i.i.d. random variables with a finite variance. This is a "two trick" proof: (a) passing to a subsequence (b) using monotone interpolation to solve the original problem. We'll see many proofs of this theorem. This version is one of the simplest and most direct.

The task then becomes the proof of the SLLN under the most natural conditions where we only assume we have a finite first moment. We'll approach this by first considering infinite sums --- of real numbers and of random variables. This will lead us to the consideration of our first maximal inequality, Kolmogorov's "Weak Type L^2" maximal inequality.

You may need to review the Cauchy criterion. The venerable Wikipedia is a little lame this time but there is a useful discussion posted by an Oxford professor. It is not sophisticated but it is worth reading.

Note: I am going to McMaster University this afternoon, so there will be no office hours today (Wednesday, 9/13).

You are encouraged to dig deeply into the differences between rabbits and hares. This is not a frivolous exercise; it can change your life. It is a fine thing to be aware of distictions --- or at least the possibility of distinctions.

## Day 3. First Look at Limit Laws

We'll revisit BCII and give a generalization. The proof illustrates two basic "tricks": The benefit of working with non-negative random variables and the juice one can get by "passing to subsequences." We'll see other versions of BCII as the course progresses.

We'll then prove a couple of easy versions of the Strong Law of Large Numbers. These results are not critical in and of themselves, but the techniques are very important. You can make a modest living with just the techniques that are covered today.

We'll may also prove a version of the Kolmogorov maximal inequality. The theory and applications of maximal inequalities is one of the big divides between elementary probability theory and graduate probability theory. Maximal inequities are not particularly hard, but they create a shift in the sophistication of the conversation.

Homework 3 is due on Monday, September 19.

Please also keep up with the blog. In particular, you should always be on the lookout for bug reports on the current homework.

## Fatou's Lemma

The Wikipedia article on Fatou's Lemma is surprisingly good. In addition to the usual proof it gives a "direct proof" (without the MCT). It also gives a version with "changing measures" that was new to me. It looks handy. Finally, it discusses the "conditional version" which we will need toward the end of the semester. Some attention is needed to the difference between the (easier) probability spaces and the (only slightly harder) spaces that do not have total mass equal to 1.

## Day 2. MCT, DCT, Fatou, Etc.

We look at the fundamental results of integration theory from the probabilist's point of view. After asking what one wants from an "expected value", we look at Lebesgue's answer --- and see why it is surprising. We'll then do "problem solving" to discover a proof of the monotone convergence theorem --- after first finding the 'baby' MCT. With the MCT in hand, Fatou's Lemma is easy. With Fatou in hand, the Dominated Convergence Theorem is easy.

We'll then look at how one can estimate some expectations and look at three very fundamental inequalities. As time permits, we'll revisit the second Borel Cantelli lemma, perhaps giving two proofs.

Homework No. 2 will be due on Monday September 12 at class time. This will put us into a regular schedule of new homework posted each Monday and due the following Monday.

## Day 1. Getting Right to Work

We will go over the plan for the course and then get right to work. The main idea is independence, and some simple questions lead to the need for some new tools. We'll also meet two of our most constant companions: the two Borel Cantelli lemmas. We'll give proofs of these, and then start looking at applications. In the course of events, you will be reminded of various ideas from real analysis, especially limsup and liminf.

Homework No. 1 is due on Wednesday September 7. Note: If sometimes you see "530" keep in mind that this is the old number for 930. I'll catch this much of the time but not all of the time.

This website is the place where one checks in to find the current homework and all of the additional information about our course, including periodic postings of supplemental material.

You can to look at the course syllabus for general information about the course as well as information about grading, homework, the midterm, and the final exam.

Please do review the course policies. I count on participants to read and follow these policies. They are quite reasonable, and it is awkward to have to single out an individual for not following our few rule.

Feel free to contact me if you have questions about the suitability of the course for you. In general the course will only be appropriate if you have had a **solid background in real analysis**, preferably at the graduate level.