Work a.t Cha.pe1 Hill sponsored by the Office of Naval Research under Contract NR 042 031. Reproduction in whole or in part is permitted for any purpose of the United States government.

2 Noise in Electrophysiological Data 2 2.1 A Concept of Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Event-Related Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Sources of Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 Strategies to Handle Noise . . . ...

NR 171-034 with Stanford University. 2. W e very much appreciate the extensive comments and criticisms of drafts of various sections that were given by Ward Edwards, F .

Lipschitz functions are interesting, but can be very rough (these can have many kinks). In many situations the functions can be much smoother. This is how you would model the temperature inside a museum room for example. Often we don’t know how smooth the function might be, so an interesting question is if we can adapt to the unknown smoothness. In this lecture we will use the Maximum Complexit...