Peter Glynn's Notes

Peter Glynn's Notes - Chapter 1 What this course is about...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Chapter 1 What this course is about This course is a Ph.D. level introduction to the key ideas and concepts related to the mathematical study of uncertainty. This area is known as probability or stochastics. There is no area of engineering, the physical sciences, economics, finance or the social sciences that has not been profoundly impacted by the use of stochastic methods. In addition, knowledge of this subject matter will change the way one approaches the formulation of mathematical and computational models. Indeed, knowledge of probability offers a new language for addressing model formulation. Stochastics also has close connections with many other areas of computational and applied mathematics: linear algebra differential equations discrete mathematics Even for areas with which one may already be familiar, knowledge of probabilistic ideas will add richness to ones understanding. A good example is the theory of least squares. We will see that the statistical perspective on least squares allows one to develop a rigorous conceptual framework that can enhance ones application of least squares (e.g. in choosing good weights for the application of weighted least squares). Our discussion of the subject matter will focus heavily on how to convert stochastic problems into computa- tions that can be addressed through numerical linear algebra or differential equations. We will also see that stochastic simulation (also know as Monte Carlo simulation) is a powerful means of doing computation in the stochastic modeling context. In addition, model building in the stochastic context often requires that we build models that respect observational data that has been collected. This model calibration issue requires that we also familiarize ourselves with the relevant statistical principles. We end this discussion with a brief illustration of the rich connections between stochastics and other areas of applied mathematics. Consider a particle that randomly moves between the sites of { , 1 , . . . , N } . See Figure 1.1 This random walker, if currently in site i , moves to its neighbors i- 1 and i + 1 with equal probability, 1 2 . The sites 0 and N are absorbing sites, so that once the particle hits either of those sites, it is absorbed into that site and never again leaves. (This is one version of the gamblers ruin model, where the random walk corresponds to the time evolution of the gamblers wealth.) One question that is of interest is the computation of u ( i ) = the probability that the random walker is absorbed into site 0 (i.e. ruined) given that they start at site i . A little thought shows that by considering the likely location of the walker after one step, the u ( j )s should satisfy: u ( i ) = 1 2 u ( i- 1) + 1 2 u ( i + 1) (1.1) 1 1 2 3 N- 1 N 1 2 1 2 1 2 1 2 1 2 1 2 1 1 Figure 1.1: Markov Chain Corresponding to States with Transition Probabilities subject to the boundary conditions...
View Full Document

This note was uploaded on 06/17/2010 for the course CME 308 taught by Professor Peterglynn during the Spring '08 term at Stanford.

Page1 / 109

Peter Glynn's Notes - Chapter 1 What this course is about...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online