O'Hagan, A. (1994). Bayesian Inference, volume 2B of Kendall's Advanced Theory of Statistics. Arnold, London. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Signal and Background Separation - von der Linden, Dose, Padayachee.. (1998) (Correct)
....(1 erf( d i Gammab i Gammaoe 2 i = p 2oe 2 i ) Gaussian e b i Q(d i 1; b i (1 1= 1 1= d i 1 ; Poisson ; 12) 7 where Q(a; x) is the regularized incomplete gamma function Gamma(a; x) Gamma(a) The entire marginal likelihood of Eq. 3) constitutes a mixture model [18] of the two cases Eq. 8) and Eq. 12) with respective weights fi = p(B i j Pi; I) and (1 Gamma fi) p(B i j Pi; I) The parameter fi completes the list Pi = fE; fig. p(djc; oe; Pi; I) Y i Theta (1 Gamma fi) p(d i jB i ; c; oe; Pi; I) fi p(d i jB i ; c; oe; Pi; I) 13) The ....
A. O'Hagan in Kendall's advanced theory of statistics, Bayesian Inference, John Wiley & Sons, New York, 1st ed., 1994.
Learning Bayesian Networks from Incomplete Data using.. - Myers, Laskey, DeJong (1999) (5 citations) (Correct)
....to find the best scoring function. In addition, without prior knowledge of structures, we can assume they have equal probability and use a noninfomative prior P(B S )1. However, if we do have information on structures we can always use the prior information. See [Gelman, Carlin et al. 1996] O Hagan 1994], and [Press 1989] for a discussion of prior probabilities and assignments of priors. The problem is now reduced to finding the structure with the maximum likelihood P(D B S ) In other words, given a structure, structures are evaluated according to how probable it is that the data were generated ....
O'Hagan, A. (1994). Bayesian Inference. London, Edward Arnold.
Estimation in Financial Models - Göing (1996) (Correct)
....basic idea of the Bayesian approach is to treat the parameter as a random variable and to use a guess or an a priori knowledge of the distribution ( of and then to estimate by calculating the a posteriori distribution ( jx) of . For details about the Bayesian theory we refer to [3] and [55]. First of all the Bayesian method will be described in the case where the parameter is onedimensional. Furthermore, the k dimensional case and the hierarchical model will be discussed. The one dimensional case In the one dimensional case the a posteriori distribution ( jx) of is calculated ....
....arise in calculating the integrals in (3.79) which require approximation techniques. We will discuss simulations of distributions using so called Markov Chain Monte Carlo (MCMC) methods. The key idea of the MCMC methods is described in the following. For details we refer to [71] 3] p. 353 or [55]. Suppose we want to generate a sample from an a posteriori distribution ( jx) for 2 Theta IR k , but cannot directly do this. However, suppose we are able to construct a Markov chain with state space Theta and with equilibrium distribution ( jx) Then under suitable regularity ....
[Article contains additional citation context not shown here]
O'Hagan, A., 1994, "Bayesian inference", Kendall's Advanced Theory of Statistics, Arnold, Vol. 2B.
Modelling heterogeneity with and without the Dirichlet process - Green, Richardson (1998) (12 citations) (Correct)
....applications listed in MacEachern and M uller (1994b) include this feature. In the DP case, this setup frequently enjoys the misleading appellation of a mixture of Dirichlet processes (MDP) model, the terminology of DP mixture models used by West, M uller and Escobar (1994) being clearer. See O Hagan (1994, pp. 288ff. for further discussion. At the top of the graph, the parameters ff, ffi and G 0 could in principle be fixed or random, and if random possibly in turn modelled hierarchically, depending on the context. Let us consider one example, that of Bayesian density estimation using a flexible ....
O'Hagan, A. (1994) Bayesian Inference (Kendall's Advanced theory of Statistics, 2 B), Wiley, New York.
Teaching Inference Using Bayes and Discrete Models - Albert (1995) (Correct)
....are examples of older texts which use this approach in different inferential settings. Due to the increasing popularity of Bayesian methods, a number of advanced introductions to Bayesian methods have recently appeared; see, for example, Berger (1985) Bernardo and Smith (1994) Lee (1989) and O Hagan (1994). It appears that few texts currently exist that introduce Bayesian statistical inference at an undergraduate level. However, Albert (1995) Berry (1995) Antleman (1995) and Lad (1995) are examples of soon to be published texts that provide a less mathematical treatment of concepts of Bayesian ....
O'Hagan, A. (1994), Bayesian Inference, Edward Arnold: Cambridge.
Modelling heterogeneity with and without the Dirichlet process - Green, Richardson (1999) (12 citations) (Correct)
....the applications listed in MacEachern and Muller (1994) include this feature. In the DP case, this setup frequently enjoys the misleading appellation of a mixture of Dirichlet processes (MDP) model, the terminology of DP mixture models used by West, Muller and Escobar (1994) being clearer. See O Hagan (1994, pp. 288ff. for further discussion. At the top of the graph, the parameters ff, ffi and G 0 could in principle be fixed or random, and if random possibly in turn modelled hierarchically, depending on the context. Let us consider one example, that of Bayesian density estimation using a flexible ....
O'Hagan, A. (1994) Bayesian Inference (Kendall's Advanced theory of Statistics, 2 B), Wiley, New York.
Assessing and Comparing Costs: How robust are the bootstrap.. - O'Hagan, Stevens (2000) Self-citation (O'hagan) (Correct)
No context found.
O'Hagan, A. (1994). Bayesian Inference, volume 2B of Kendall's Advanced Theory of Statistics. Arnold, London.
Bayesian Assessment of Goodness-of-Fit against.. - Conigliani, Castro.. (2000) Self-citation (O'hagan) (Correct)
....a belief about how close the true distribution should be to a member of the parametric family, if in fact the alternative model holds. As c increases (keeping the relative magnitude c j c fixed) the prior variances of the # j s decrease and the # j s become closer to the 12 # 0 j (#)s (O Hagan 1994). In the limit, as c goes to infinity, the alternative model coincides with M 1 , which may be confirmed by seeing that the fractional Bayes factor converges to 1. In fact, applying Stirling series, we can write q 2 (r; b) # n c bn c # c 0.5 ## j # r j c# 0 j (#) n c # r j # r ....
O'Hagan, A. (1994). Bayesian Inference. Kendall's Advanced Theory of Statistics,vol.
Bayesian Assessment of Goodness-of-Fit against.. - Castro, Conigliani.. (1998) Self-citation (O'hagan) (Correct)
No context found.
O'Hagan, A. (1994). Bayesian Inference. Kendall's Advanced Theory of Statistics, vol. 2B. London: Arnold.
Choice of Basis for Laplace Approximation - MacKay (1998) (6 citations) (Correct)
No context found.
Kluwer. O'Hagan, A. (1994). Bayesian Inference, volume 2B of Kendall's Advanced Theory of Statistics.
Monte Carlo Implementation of Gaussian Process Models for Bayesian .. - Neal (1997) (47 citations) (Correct)
No context found.
O'Hagan, A. (1994) Bayesian Inference (Volume 2B in Kendall's Advanced Theory of Statistics) .
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC