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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Acronyms
| ABS | asset-backed security |
| AR | autoregressive |
| ARCH | autoregressive conditional heteroskedasticity |
| ARIMA | autoregressive integrated moving averages |
| BB1 | Joe’s BB1 |
| BG | beta-geometric mixture model |
| BUGS | Bayes using Gibbs sampler (software) |
| CDO | collateralized debt obligation |
| CDS | credit default swap |
| CFTP | coupling from the past |
| CLT | the Central Limit Theorem |
| CMLE | canonical maximum likelihood estimation (estimates) |
| c.d.f. | cumulative distribution function |
| ENIAC | electronic numerical integrator and computer |
| FGM | Farlie–Gumbel–Morgenstern copulas |
| FTD | first-to-default (swaps) |
| GARCH | generalized autoregressive conditional heteroskedasticity |
| HAC | hierarchical Archimedean copula |
| HKC | hierarchical Kendall copula |
| IFM | inference from margins |
| i.i.d. | independent identically distributed |
| IMA | independent Metropolis algorithm |
| ISBA | International Society for Bayesian Analysis |
| JAGS | just another Gibbs sampler (software) |
| LLN | the Law of Large Numbers |
| MANIAC | mathematical analyzer, numerator, integrator, and computer |
| MCMC | Markov chain Monte Carlo |
| MHA | Metropolis–Hastings algorithm |
| MLE | maximum likelihood estimation (estimates) |
| MPLE | maximum pseudo-likelihood estimation (estimates) |
| PCC | pair copula construction |
| PD | probability of default |
| p.d.f | probability density function |
| RLUF | Rodriguez Lallena and Ubeda Flores copulas |
| RTO | regression through the origin |
| RWMHA | random walk Metropolis–Hastings algorithm |
| SACF | sample autocorrelation function |
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.