-
1
-
-
33644967739
-
Efficient Bayes factor estimation from the reversible jump output
-
BARTOLUCCI, F., SCACCIA, L. & MIRA, A. (2006). Efficient Bayes factor estimation from the reversible jump output. Biometrika 93, 41-52.
-
(2006)
Biometrika
, vol.93
, pp. 41-52
-
-
Bartolucci, F.1
Scaccia, L.2
Mira, A.3
-
2
-
-
2242491935
-
Computational and inferential difficulties with mixtures posterior distribution
-
CELEUX, G., HURN, M. & ROBERT, C. (2000). Computational and inferential difficulties with mixtures posterior distribution. J. Am. Statist. Assoc. 95, 957-979
-
(2000)
J. Am. Statist. Assoc.
, vol.95
, pp. 957-979
-
-
Celeux, G.1
Hurn, M.2
Robert, C.3
-
3
-
-
0031527297
-
On Monte Carlo methods for estimating ratios of normalizing constants
-
CHEN, M. & SHAO, Q. (1997). On Monte Carlo methods for estimating ratios of normalizing constants. Ann. Statist. 25, 1563-1594
-
(1997)
Ann. Statist.
, vol.25
, pp. 1563-1594
-
-
Chen, M.1
Shao, Q.2
-
5
-
-
0041974049
-
Marginal likelihood from the Gibbs output
-
CHIB, S. (1995). Marginal likelihood from the Gibbs output. J. Am. Statist. Assoc. 90, 1313-1321
-
(1995)
J. Am. Statist. Assoc.
, vol.90
, pp. 1313-1321
-
-
Chib, S.1
-
6
-
-
0001244941
-
Estimation of finite mixture distributions by Bayesian sampling
-
DIEBOLT, J. & ROBERT, C. (1994). Estimation of finite mixture distributions by Bayesian sampling. J. R. Statist. Soc. B 56, 363-375
-
(1994)
J. R. Statist. Soc. B
, vol.56
, pp. 363-375
-
-
Diebolt, J.1
Robert, C.2
-
7
-
-
72949112934
-
Discussion of Nested sampling for Bayesian computations by John Skilling
-
Ed. J. Bernardo, M. Bayarri, J. Berger, A. David, D. Heckerman, A. Smith and M. West, Oxford: Oxford University Press
-
EVANS, M. (2007). Discussion of Nested sampling for Bayesian computations by John Skilling. In Bayesian Statistics 8, Ed. J. Bernardo, M. Bayarri, J. Berger, A. David, D. Heckerman, A. Smith and M. West, pp. 491-524. Oxford: Oxford University Press.
-
(2007)
Bayesian Statistics
, vol.8
, pp. 491-524
-
-
Evans, M.1
-
8
-
-
33750369868
-
Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques
-
FRÜHWIRTH-SCHNATTER, S. (2004). Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques. Econometr. J. 7, 143-167
-
(2004)
Econometr. J.
, vol.7
, pp. 143-167
-
-
Frühwirth-Schnatter, S.1
-
9
-
-
0000539315
-
Bayesian model choice: Asymptotics and exact calculations
-
GELFAND, A. & DEY, D. (1994). Bayesian model choice: asymptotics and exact calculations. J. R. Statist. Soc. B 56, 501-514
-
(1994)
J. R. Statist. Soc. B
, vol.56
, pp. 501-514
-
-
Gelfand, A.1
Dey, D.2
-
11
-
-
77956889087
-
Reversible jump MCMC computation and Bayesian model determination
-
GREEN, P. (1995). Reversible jump MCMC computation and Bayesian model determination. Biometrika 82, 711-732
-
(1995)
Biometrika
, vol.82
, pp. 711-732
-
-
Green, P.1
-
12
-
-
0442312140
-
MCMC methods for computing Bayes factors: A comparative review
-
HAN, C. & CARLIN, B. (2001). MCMC methods for computing Bayes factors: a comparative review. J. Am. Statist. Assoc. 96, 1122-1132
-
(2001)
J. Am. Statist. Assoc.
, vol.96
, pp. 1122-1132
-
-
Han, C.1
Carlin, B.2
-
13
-
-
78649424388
-
Weighted average importance sampling and defensive mixture distributions
-
HESTERBERG, T. (1995). Weighted average importance sampling and defensive mixture distributions. Technometrics 37, 185-194
-
(1995)
Technometrics
, vol.37
, pp. 185-194
-
-
Hesterberg, T.1
-
14
-
-
22544479764
-
Markov Chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
-
JASRA, A., HOLMES, C. & STEPHENS, D. (2005). Markov Chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statist. Sci. 20, 50-67.
-
(2005)
Statist. Sci.
, vol.20
, pp. 50-67
-
-
Jasra, A.1
Holmes, C.2
Stephens, D.3
-
15
-
-
0003414592
-
-
1st ed. Oxford: The Clarendon Press
-
JEFFREYS, H. (1939). Theory of Probability, 1st ed. Oxford: The Clarendon Press.
-
(1939)
Theory of Probability
-
-
Jeffreys, H.1
-
17
-
-
0001044972
-
Finding the observed information matrix when using the em algorithm
-
LOUIS, T. (1982). Finding the observed information matrix when using the EM algorithm. J. R. Statist. Soc. B 44, 226-233
-
(1982)
J. R. Statist. Soc. B
, vol.44
, pp. 226-233
-
-
Louis, T.1
-
20
-
-
21444451325
-
Simulating ratios of normalizing constants via a simple identity: A theoretical exploration
-
MENG, X. & WONG, W. (1996). Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statist. Sinica 6, 831-860
-
(1996)
Statist. Sinica
, vol.6
, pp. 831-860
-
-
Meng, X.1
Wong, W.2
-
21
-
-
33644772615
-
A nested sampling algorithm for cosmological model selection
-
MUKHERJEE, P., PARKINSON, D. & LIDDLE, A. (2006). A nested sampling algorithm for cosmological model selection. Astrophys. J. 638, L51-L54.
-
(2006)
Astrophys. J.
, vol.638
-
-
Mukherjee, P.1
Parkinson, D.2
Liddle, A.3
-
22
-
-
77955876928
-
Nested sampling for Potts models
-
Ed. Y. Weiss, B. Schölkopf and J. Platt. Cambridge, MA: MIT Press
-
MURRAY, I.,MACKAY, D. J.,GHAHRAMANI, Z. & SKILLING, J. (2006). Nested sampling for Potts models. In Advances in Neural Information Processing Systems 18, Ed. Y. Weiss, B. Schölkopf and J. Platt. Cambridge, MA: MIT Press.
-
(2006)
Advances in Neural Information Processing Systems
, vol.18
-
-
Murray, I.1
MacKay, D.J.2
Ghahramani, Z.3
Skilling, J.4
-
23
-
-
0000273048
-
Annealed importance sampling
-
NEAL, R. (2001). Annealed importance sampling. Statist. Comp. 11, 125-139
-
(2001)
Statist. Comp.
, vol.11
, pp. 125-139
-
-
Neal, R.1
-
24
-
-
1842607847
-
-
R Development Core Team, Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. URL
-
R Development Core Team (2010). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. URL: http://www.R-project.org.
-
(2010)
R: A Language and Environment for Statistical Computing
-
-
-
25
-
-
18244378520
-
On Bayesian analysis of mixtures with an unknown number of components (with discussion)
-
RICHARDSON, S. & GREEN, P. (1997). On Bayesian analysis of mixtures with an unknown number of components (with discussion). J. R. Statist. Soc. B 59, 731-792
-
(1997)
J. R. Statist. Soc. B
, vol.59
, pp. 731-792
-
-
Richardson, S.1
Green, P.2
-
27
-
-
0039425350
-
Convergence of slice sampler Markov chains
-
ROBERTS, G. & ROSENTHAL, J. (1999). Convergence of slice sampler Markov chains. J. R. Statist. Soc. B 61, 643-660
-
(1999)
J. R. Statist. Soc. B
, vol.61
, pp. 643-660
-
-
Roberts, G.1
Rosenthal, J.2
-
28
-
-
0001455583
-
Parameter estimation for Markov modulated Poisson processes
-
RYD́EN, T. (1994). Parameter estimation for Markov modulated Poisson processes. Stochastic Models 10, 795-829.
-
(1994)
Stochastic Models
, vol.10
, pp. 795-829
-
-
Ryd́en, T.1
-
29
-
-
34547375807
-
Efficient Bayesian inference for multimodal problems in cosmology
-
SHAW, J., BRIDGES, M. & HOBSON, M. (2007). Efficient Bayesian inference for multimodal problems in cosmology. Mon. Not. R. Astron. Soc. 378, 1365-1370
-
(2007)
Mon. Not. R. Astron. Soc.
, vol.378
, pp. 1365-1370
-
-
Shaw, J.1
Bridges, M.2
Hobson, M.3
-
30
-
-
35148901361
-
Nested sampling for general Bayesian computation
-
SKILLING, J. (2006). Nested sampling for general Bayesian computation. Bayesian Anal. 1, 833-860
-
(2006)
Bayesian Anal
, vol.1
, pp. 833-860
-
-
Skilling, J.1
-
31
-
-
72949108545
-
Nested sampling's convergence
-
New York: AIP
-
SKILLING, J. (2009). Nested sampling's convergence. In AIP Proc. 1193, pp. 277-291 New York: AIP.
-
(2009)
AIP Proc
, vol.1193
, pp. 277-291
-
-
Skilling, J.1
-
32
-
-
0036435040
-
Bayesian measures of model complexity and fit (with discussion)
-
SPIEGELHALTER, D. J.,BEST, N. G.,CARLIN, B. P. & VAN DER LINDE, A. (2002). Bayesian measures of model complexity and fit (with discussion). J. R. Statist. Soc. B 64, 583-639.
-
(2002)
J. R. Statist. Soc. B
, vol.64
, pp. 583-639
-
-
Spiegelhalter, D.J.1
Best, N.G.2
Carlin, B.P.3
Van Der Linde, A.4
-
33
-
-
58149402004
-
Bayesian strong gravitational-lens modelling on adaptive grids: Objective detection of mass substructure in galaxies
-
VEGETTI, S. & KOOPMANS, L. V. E. (2009). Bayesian strong gravitational-lens modelling on adaptive grids: objective detection of mass substructure in galaxies. Mon. Not. R. Astron. Soc. 392, 945-963
-
(2009)
Mon. Not. R. Astron. Soc.
, vol.392
, pp. 945-963
-
-
Vegetti, S.1
Koopmans, L.V.E.2
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