Bayesian estimation of NIG models via Markov chain Monte Carlo methods
✍ Scribed by Dimitris Karlis; Jostein Lillestöl
- Publisher
- John Wiley and Sons
- Year
- 2004
- Tongue
- English
- Weight
- 243 KB
- Volume
- 20
- Category
- Article
- ISSN
- 1524-1904
- DOI
- 10.1002/asmb.544
No coin nor oath required. For personal study only.
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The marked increase in popularity of Bayesian methods in statistical practice over the last decade owes much to the simultaneous development of Markov chain Monte Carlo (MCMC) methods for the evaluation of requisite posterior distributions. However, along with this increase in computing power has co
The level of mathematics used is rather variable. The authors state (p. 10) &the expectations are E(xN )" and E(s)" ', without ever de"ning expectation. Matrix notation is used for two relatively short sections on response surface designs (pp. 169}173) and general block designs (pp. 221}240), but no