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This is fabulas book. The editor-in-Chief is  Timothy Gowers who need no introduction and June Barrow-Green is an assistant editor (with particular expertise in the history of mathematics). The central focus of the book will be to describe modern pure mathematics, in all its diversity, in a way that is serious, sometimes quite detailed, but always accessible at the lowest possible level. It is very helpful for person like me in particular, who love math and finding ways to study math seriously, as the book take each topic very seriously, trying to be understandable even to the beginner (fundamental definitions are explained clearly with follow up examples) and explanations are given in considerably detail. 


There is a website dedicated to it. To promote this great publication, I have no mean to keep its website secret– PCM. Furthermore, You can get into this site with userid Guest and password PCM (at least it works for this moment, I hope it will be forever). 

Many subtle definitions, such as interior point, accumulation point, open set, closed set, are all based on the concepts of distance and neighborhoods.  The beauty is mathematics is you could always go further with good understanding of the fundamental ideas by going into general. The metric space is a good example of the generalization of ideas, from distance to metric . Further to the metric space. Most concepts which have been defined under the framework of distance and neighborhood thus can be extended to this more general setting. This is one way mathematics extended to more general setting and developed into new math branch.  

DEFINITION 1 A probability space (\Omega, \mathcal F, P) is a triplet of a set \Omega, a family \mathcal F of subsets \Omega, and a mapping P from \mathcal F into \mathbb R satisfying the following conditions: Read the rest of this entry »

Convergence in distribution

Suppose that F1F2, … is a sequence of cumulative distribution functions corresponding to random variables X1X2, …, and that F is a distribution function corresponding to a random variable X. We say that the sequence Xn converges towards X in distribution, if

\lim_{n\rightarrow\infty} F_n(a) = F(a),

for every real number a at which F is continuous. Since F(a) = Pr(X ≤ a), this means that the probability that the value of X is in a given range is very similar to the probability that the value of Xn is in that range, provided n is sufficiently large. Convergence in distribution is often denoted by adding the letter \mathcal D over an arrow indicating convergence:

X_n \, \xrightarrow{\mathcal D} \, X

Small d is also possible, although less common.

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