March 2017
Beginner to intermediate
352 pages
9h 18m
English
book
Introduction to Bayesian Estimation and Copula Models of Dependence
by Arkady Shemyakin, Alexander Kniazev
Content preview from Introduction to Bayesian Estimation and Copula Models of Dependence
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Glossary
| E(X) | expected value (mean) |
| Var(X) | variance |
| Bin(n, p) | binomial distribution with n trials and success probability p |
| Poiss(λ) | Poisson distribution |
| NB(p, r) | negative binomial distribution |
| N(μ, σ2) | normal (Gaussian) distribution with mean μ and variance σ2 |
| T(η) | (Student) t-distribution with η degrees of freedom |
| Gamma(z) | gamma function |
| Gamma(α, λ) | gamma distribution with shape α and rate λ |
| Beta(α, β) | beta distribution |
| Cov(X, Y) | covariance |
| ρ(X, Y) | correlation |
| MN(μ, Σ) | multivariate normal distribution |
| L(x; θ) | likelihood function with data x and parameter θ |
| Λ(x) | likelihood ratio |
| PG(1, r) | polygamma function order r |
| X ∼ F | random variable X has distribution F |
| ∝ | proportional |
| ⪯ | less or equal in sense of a special natural ordering |
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.