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Apr 9, 2011 · 1. @Kurapika I took a quick look at both books. Seems the "Models" book is more oriented towards teaching probability for application in engineering, while "First Course" is more 'pure probability'. More precisely, "First Course" seems to be more rigorous, while "Models" seems to cover more ground in roughly the same amount of pages, e.g ...
Probability (which one(s)?): An Introduction to Probability Theory and Its Applications, Vol. 1 and Vol. 2 by Feller (for intuitive understanding) Introduction to Probability Theory by Hoel, Port, Stone; A Probability Path by Resnick (for measure theoretic / modern approach?) Fifty Challenging Problems in Probability by Mosteller
Questions tagged [probability] Ask Question. For questions about probability. independence, total probability and conditional probability. For questions about the theoretical footing of probability use [tag:probability-theory]. For questions about specific probability distributions, use [tag:probability-distributions]. Learn more….
The probability density function for a given value of random variable X represents the density of probability (probability per unit random variable) within a particular range of that random variable X. Now, I don't know how the p.d.f. can take value larger than 1. It is in this sense that probability density can take values larger than 1.
My solution was to first find, as above, the probability of only one of the events occurring, which is P(A ∪ B) − P(A ∩ B) =.8 P (A ∪ B) − P (A ∩ B) =.8. Then, the probability of only A occurring is the probability of A occurring given that only one of the events will occur, or P(A ∣ S) P (A ∣ S), where S is the event that only ...
Here is a list of great books in probability, found in this blog: The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!) An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition. Discovering Statistics Using R.
Random variable. A random variable X is defined as a map X: Ω → R such that, for any x ∈ R, the set {ω ∈ Ω ∣ X(ω) ≤ x} is an element of A, ergo, an element of Pr 's domain to which a probability can be assigned. We can think of X as a "realisation" of Ω, in that it assigns a real number to each outcome in Ω.
Oct 10, 2019 · The 'opposite' (complement) of winning at least once is never winning at all. The probability of not picking the winning ball the first time is $1-0.04=0.96$, i.e. $96\%$. But you also want this to happen the second time and the third time. So you do not win with probability $$ 0.96 \cdot 0.96 \cdot 0.96= 0.884736, $$ i.e. $88.4736\%$.
Oct 16, 2021 · 5. The most basic difference between probability mass function and probability density function is that probability mass function concentrates on a certain point for example, if we have to find a probability of getting a number 2. Then our whole concentration is on 2. Hence we use pmf however in pdf our concentration our on the interval it is ...
Aug 14, 2015 · One of the property of Independent events is that the probability of their intersection is a product of their individual probabilities. So, P(A ∩ B) P (A ∩ B) is P(A) × P(B) P (A) × P (B). Whereas for mutually exclusive events, the probability of intersection is 0 0 as they can't both occur simultaneously! P(A ∪ B ∪ C) = P(A) + P(B ...
