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A short introduction to probability for statistics
A short introduction to probability for statistics

Lecture 7: Chernoff`s Bound and Hoeffding`s Inequality 1 Developing
Lecture 7: Chernoff`s Bound and Hoeffding`s Inequality 1 Developing

First Return Probabilities - University of California, Berkeley
First Return Probabilities - University of California, Berkeley

214.7 (MH) - Duke Statistical
214.7 (MH) - Duke Statistical

The Rate of Convergence of k, -NN Regression
The Rate of Convergence of k, -NN Regression

... One has to emphasize that (2) is a smoothing condition, However, it does not imply that m is continuous mod p. If Jk7;= (n/k,)“/d, i.e., we choose k, = nV(~+(W4) then according to (3) the rate of convergence in probability least n - I/P+(d/a)) ...
Study on the Dependence Conditional Risk in Financial Portfolio
Study on the Dependence Conditional Risk in Financial Portfolio

... X ≤ x or Y ≤ y and X > x or Y > y or ( X > x, Y > y ) . The structure of this paper is as follows. Section 2 introduces the notation of copula and some tail dependence measures. Section 3 presents several conditional dependence risk probability functions and conditional dependence risk density funct ...
Sampling Theory VARYING PROBABILITY SAMPLING
Sampling Theory VARYING PROBABILITY SAMPLING

What Conditional Probability Must (Almost)
What Conditional Probability Must (Almost)

... I will concede all of the specific intuitions that Hájek uses in his paper. However, I will show that any natural generalization of these intuitions will lead to an inconsistency in one situation that he discusses. Suppose that we have a uniform probability measure over the Earth’s surface (imagine ...
A History and Introduction to the Algebra of Conditional Events and
A History and Introduction to the Algebra of Conditional Events and

The Modelling of Random Phenomena
The Modelling of Random Phenomena

... We may think that a die is balanced when in fact it is not. In this case, the relative frequencies will not converge to the probabilities that we expect. Or, plainly, we suspect that the die is not balanced, and we do not know what to expect. In any case, the Law of Large Numbers leads to the follow ...
Branching Processes with Negative Offspring Distributions
Branching Processes with Negative Offspring Distributions

A and B
A and B

... outcomes could happen, but we don’t know which particular outcome did or will happen. In general, each occasion upon which we observe a random phenomenon is called a trial. At each trial, we note the value of the random phenomenon, and call it an outcome. When we combine outcomes, the resulting comb ...
8.6 Practice set 3 - School District 27J
8.6 Practice set 3 - School District 27J

... 1. What is the probability of exactly 3 students buying a bag of trail mix? a. What is the probability of each student buying a bag of trail mix? b. Define each variable used in the formula and give its value. c. Write the binomial formula, substituting these values. __________________________ d. So ...
On the Reliability of Clustering Stability in the Large A
On the Reliability of Clustering Stability in the Large A

... from each strip, because the intersection of these different strips is asymptotically negligible. All the other lemmas in Subsec. D.2 develop technical results needed for our proof. Finally, let us describe the proof of Lemma D.5 in a bit more detail. It starts with an expression equivalent to the o ...
A Guided Tour of Sets, Functions, and Random Variables
A Guided Tour of Sets, Functions, and Random Variables

... Figure 5: The convention for drawing diagrams Later in these notes, we will create complex experiments from simple experiments. The definitions of complex experiments will be easier to understand with the help of diagrams. The convention we will adopt for diagrams is the following: We will draw two ...
Normal approximation of Binomial probabilities
Normal approximation of Binomial probabilities

Some Typical Properties of Large AND/OR Boolean Formulas 1
Some Typical Properties of Large AND/OR Boolean Formulas 1

Full Text PDF
Full Text PDF

Return-time statistics, Hitting
Return-time statistics, Hitting

Philosophy of Science, 69 (September 2002) pp
Philosophy of Science, 69 (September 2002) pp

... is an unexpected feature of finitely additive probability measures on a product space X × Y that not all such probabilities are strategic. In the martingale convergence theorem, probability statements are made about infinite sequences of observations. Such probabilities are built up from probabiliti ...
On the Genealogy of Large Populations Author(s): J. F. C. Kingman
On the Genealogy of Large Populations Author(s): J. F. C. Kingman

... it implies that two successive mutations will with probability cancel each other out and restore the original allele. In fact, such returns to the origin probably occur most infrequently, and a model at the other extreme, ignoring recurrent mutation, is more plausible as well as mathematically simpl ...
Discrete random variables and their expectations
Discrete random variables and their expectations

... We now define the important notion of independence of random variables. We start with a general definition that applies to all types of random variables, in­ cluding discrete and continuous ones. We then specialize to the case of discrete random variables. 3.1 Independence of general random variables ...
Lecture 1
Lecture 1

Epidemic on Reed-frost Random Intersection Graph with Tunable
Epidemic on Reed-frost Random Intersection Graph with Tunable

1 Approximate Counting by Random Sampling
1 Approximate Counting by Random Sampling

... Suppose there is a bag containing red balls and blue balls. You would like to estimate the fraction of red balls in the bag. However, you are only allowed to sample randomly from the bag with replacement. The straightforward method is to make T random samples and use the fraction of red balls in the ...
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Conditioning (probability)

Beliefs depend on the available information. This idea is formalized in probability theory by conditioning. Conditional probabilities, conditional expectations and conditional distributions are treated on three levels: discrete probabilities, probability density functions, and measure theory. Conditioning leads to a non-random result if the condition is completely specified; otherwise, if the condition is left random, the result of conditioning is also random.This article concentrates on interrelations between various kinds of conditioning, shown mostly by examples. For systematic treatment (and corresponding literature) see more specialized articles mentioned below.
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