Slide 14.4.1 Cleaning up some details of our implementation So we have seen a first pass at building an object-oriented tem, using Scheme as the base. There are a few details that The need for self-reference

Slide 12.1.1 In the last lecture, we introduced mutation as a component of 6001s|cP our data structures We saw for example that set was a Environment mode way of changing the value associated with a variable in our system, and we saw that set-car! and set-cdr! were ways of changing the values of parts of list structure Now, several important things happened when we introduced

Disioint-set data structure (Union-Find) Problem: maintain a dynamic collection of pairwise-disjoint sets S=(S Each set S; has one element distinguished as the representative element, rep[sil lust support 3 operations

Ch. 5 Hypothesis Testing The current framework of hypothesis testing is largely due to the work of Neyman and Pearson in the late 1920s, early 30s, complementing Fisher's work on estimation. As in estimation, we begin by postulating a statistical model but instead of seeking an estimator of 6 in e we consider the question whether

Ch. 22 Unit root in Vector Time series 1 Multivariate Wiener Processes and multivari- ate FCLT Section 2.1 of Chapter 21 described univariate standard Brownian motion W(r) as a scalar continuous-time process(W: rE0, 1-R). The variable W(r) has a N(O, r)distribution across realization, and for any given realization, w(r) is continuous function of the date r with independent increments. If a set of k such independent processes, denoted

Ch. 15 Forecasting Having considered in Chapter 14 some of the properties of ARMA models, we now show how they may be used to forecast future values of an observed time series. For the present we proceed as if the model were known ecactly Forecasting is an important concept for the studies of time series analysis. In the scope of regression model we usually

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Topic 1: What is Corporate Finance?(cont 1 2 Financing Decisions In the previous section the focus was on investment decisions. It was assumed throughout that the firm was financed with equity. In this section we discuss financing decisions. As a prelude to this we will briefly discuss the notion of efficient

Noise is additional\unwanted \signal that interferes with the transmitted signal Generated by electronic devices The noise is a random process Each\sample \ of n(tis a random variable Typically, the noise process is modeled as\Additive White Gaussian noise”(AWGN) White: Flat frequency spectrur Gaussian noise distribution

Proof Existence and uniqueness of r(t, u)and A(t)follow from Theorem 3. 1. Hence, in order to prove differentiability and the formula for the derivative, it is sufficient to show that there exist a function C: R++R+ such that C(r)/r-0 as r-0 and E>0 such