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Let's go ahead and get started. OK, so today we have one topic to finish up very briefly from last time. So if you remember, when we finished off last time, we were talking about the example of a multithreaded Web server. So the example that we were talking about, and this is an example that I'm going to use throughout the lecture today consisted of a Web server with, this example consists of a Web server with three main modules or main components. So it consists, the three modules are a networking module, a Web server module -- -- which is in charge of generating, for example, HTML pages, and then a disk module which is in charge of reading data off a disk. OK, so this thing is going to be communicating with the disk, which I've drawn as a cylinder here. So what happens is that client requests come in to this Web server. They come in to the network module. The network module forwards those requests on to the Web server. The Web server is in charge of generati...

Let's go ahead and get started, you guys. OK, so before we begin, I just want a brief note about the quiz. So you should get the quiz back tomorrow in recitation. The mean and median on the quiz were 69 out of 100, so look for it tomorrow. And there should be solutions post it also tomorrow. Does anybody or member with standard deviation was on the quiz? OK, thanks. OK, so the topic for today, we're going to continue talking about networking. And just to recap where we left off last time, I wanted to review a few of the best effort network properties. So you remember at the end of lecture last time we talked about this notion of a best effort network. And we said that these packet switch networks like the Internet are typically best effort, which means that there are certain things in particular that they don't guarantee. So one property of best effort networks is that they're subject to delays. So these are delays due to the propagation of messages down o...

We can finally go ahead and introduce the basic elements of the Bayesian inference framework. There is an unknown quantity, which we treat as a random variable, and this is what's special and why we call this the Bayesian inference framework. This is in contrast to other frameworks in which the unknown quantity theta is just treated as an unknown constant. But here, we treat it as a random variable, and as such, it has a distribution. This is the prior distribution. This is what we believe about Theta before we obtain any data. And then, we obtain some data, which are some observation. That observation is a random variable, but when the process gets realized, we observe an actual value, numerical value, of this random variable. The observation process is modeled, again in terms of a probabilistic model. We specify the distribution of X, but we actually specify the conditional distribution of X. We say how X will behave if Theta happens to take on a specific value. The...

JUDY HOYT: Go ahead and get started. We want to start with looking at the schedule, get ourselves reoriented. This is November 16. And we'll have our final lecture today on chapter 9. Chapter 9 has three lectures. We'll talk about modeling the topography of deposition. And you're working on-- hopefully everybody's working on homework number five. That homework is due next lecture on the 18th. And does anybody have any questions on the homework? Or if you do, make sure you see me after class or send me an email. In addition to the homework, everyone has now-- except one person on the clipboard-- has a topic. And I had my assistant type it up. I'll put it on the web. Everybody's signed up for a final report topic. And you can verify that she got it right. And then if you're doing an oral presentation, this week I'll start making up a schedule so you know which day you're going to speak. OK. So hopefully everybody recovered from the snowst...