Sequentially-Allocated Merge-Split Sampler for Conjugate and Nonconjugate Dirichlet Process Mixture Models

A camera system comprises a camera for photographing an object and acquiring image data of the object, a computer having a connector which allows the camera to be connected to the computer, a mode setting device provided to the camera and for setting an operation mode of the camera, a display which functions together with the computer, and a controller provided to the computer. The controller detects a connection between the camera and the computer via the connector, and causes an image representing the mode setting device and an image representing a display panel for displaying the image taken by the camera to be displayed upon detection of the connection. When the computer is manipulated for setting a mode through the image representing the mode setting device, the controller supplies an instruction signal for setting the selected mode to the camera, and the camera sets the mode in response to the instruction supplied from the controller.

[1]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[2]  Radford M. Neal.  Bayesian Mixture Modeling , 1992 .

[3]  M. Escobar.  Estimating Normal Means with a Dirichlet Process Prior , 1994 .

[4]  K. Roeder.  Density estimation with confidence sets exemplified by superclusters and voids in the galaxies , 1990 .

[5]  S. MacEachern.  Estimating normal means with a conjugate style dirichlet process prior , 1994 .

[6]  Jun S. Liu.  Nonparametric hierarchical Bayes via sequential imputations , 1996 .

[7]  C. Robert,et al.  Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .

[8]  D. Blackwell,et al.  Ferguson Distributions Via Polya Urn Schemes , 1973 .

[9]  Jun S. Liu,et al.  Sequential importance sampling for nonparametric Bayes models: The next generation , 1999 .

[10]  Radford M. Neal,et al.  A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model , 2004 .

[11]  P. Damlen,et al.  Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables , 1999 .

[12]  C. Antoniak.  Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[13]  Steven N. MacEachern,et al.  Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models , 2000 .

[14]  Persi Diaconis,et al.  Spectral Analysis for Discrete Longitudinal Data , 1994 .

[15]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo in Practice: A Roundtable Discussion , 1998 .

[16]  R. M. Korwar,et al.  Contributions to the Theory of Dirichlet Processes , 1973 .

[17]  S. MacEachern,et al.  A semiparametric Bayesian model for randomised block designs , 1996 .

[18]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[19]  M. Newton,et al.  Computational Aspects of Nonparametric Bayesian Analysis with Applications to the Modeling of Multiple Binary Sequences , 2000 .

[20]  Jun S. Liu,et al.  The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .