J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

D. Dunson and N. Pillai, “Bayesian density regression,” J. R. Stat. Soc. 69, 163–183 (2007).

[CrossRef]

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

K. Corsey and A. Webb, “Bayesian gamma mixture model approach to radar target recognition,” IEEE Trans. Aerosp. Electron. Syst. 39, 1201–1217 (2003).

D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003).

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

H. Ishwaran and M. Zarepour, “Exact and approximate sum representations for the Dirichlet process,” Can. J. Stat. 30, 269–283 (2002).

[CrossRef]

M. Wiper, D. Insua, and F. Ruggeri, “Mixtures of gamma distributions with applications,” J. Comput. Graph. Stat. 10, 440–454 (2001).

[CrossRef]

M. Al-Habash, L. Andrews, and R. Phillips, “Mathematical model for the irradiance probability density function of a laser beam propagating through turbulent media,” J. Opt. Eng. 40, 1554–1562 (2001).

[CrossRef]

A. Webb, “Gamma mixture models for target recognition,” Pattern Recogn. 33, 2045–2054 (2000).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

R. Barakat, “Second-order statistics of integrated intensities and detected photons, the exact analysis,” J. Mod. Opt. 43, 1237–1252 (1996).

[CrossRef]

M. Escobar and M. West, “Bayesian density estimation and inference using mixtures,” J. Am. Stat. Assoc. 90, 577–588 (1995).

[CrossRef]

J. Sethuraman, “A constructive definition of Dirichlet priors,” Statistica Sinica 4, 639–650 (1994).

A. Gelfand and A. Smith, “Sampling-based approaches to calculating marginal densities,” J. Am. Stat. Assoc. 85, 398–409 (1990).

[CrossRef]

J. Churnside and R. Hill, “Probability density of irradiance scintillations for strong path-integrated refractive turbulence,” J. Opt. Soc. Am. 4, 727–733 (1987).

[CrossRef]

M. Aitkin and D. Rubin, “Estimation and hypothesis testing in finite mixture models,” J. R. Stat. Soc. 47, 67–75 (1985).

E. Jakeman and P. Pusey, “Significance of the k-distribution in scattering experiments,” Phys. Rev. Lett. 40, 546–550 (1978).

[CrossRef]

A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39, 1–38 (1977).

W. Strohbein, T. Wang, and J. Speck, “On the probability distribution of line-of-sight fluctuations for optical signals,” Radio Sci. 10, 59–70 (1975).

W. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika 57, 97–109 (1970).

[CrossRef]

E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Sci. 33, 1065–1076 (1962).

[CrossRef]

M. Rosenblatt, “Remarks on some nonparametric estimates of density function,” Ann. Math. Sci. 27, 832–837 (1956).

[CrossRef]

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

J. Neyman and E. Pearson, “On the problem of the most efficient tests of statistical hypothesis,” Philos. Trans. R. Soc. London 231, 289–337 (1933).

[CrossRef]

M. Aitkin and D. Rubin, “Estimation and hypothesis testing in finite mixture models,” J. R. Stat. Soc. 47, 67–75 (1985).

M. Al-Habash, L. Andrews, and R. Phillips, “Mathematical model for the irradiance probability density function of a laser beam propagating through turbulent media,” J. Opt. Eng. 40, 1554–1562 (2001).

[CrossRef]

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

O. Korotkova, S. Avramov-Zamurovic, R. Malek-Madani, and C. Nelson, “Probability density function of the intensity of a laser beam propagating the maritime environment,” Opt. Express 19, 20322–20331 (2011).

[CrossRef]

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

M. J. Beal, “Variational algorithms for approximate Bayesian inference,” Ph.D. Thesis (University College London, 2003).

D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003).

P. Orbanz and J. Buhmann, “Smooth image segmentation by nonparametric Bayesian inference,” in European Conference on Computer Vision (Springer, 2006), Vol. 1, pp. 444–457.

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

E. Wang, D. Liu, J. Silva, and L. Carin, “Joint analysis of time-evolving binary matrices and associated documents,” in Advances in Neural Information Processing Systems (Curran Associates, 2010), pp. 2370–2378.

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

K. Corsey and A. Webb, “Bayesian gamma mixture model approach to radar target recognition,” IEEE Trans. Aerosp. Electron. Syst. 39, 1201–1217 (2003).

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39, 1–38 (1977).

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

D. Dunson and N. Pillai, “Bayesian density regression,” J. R. Stat. Soc. 69, 163–183 (2007).

[CrossRef]

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

M. Escobar and M. West, “Bayesian density estimation and inference using mixtures,” J. Am. Stat. Assoc. 90, 577–588 (1995).

[CrossRef]

T. Ferguson, “Bayesian density estimation by mixtures of normal distributions,” Recent Advances in Statistics (Academic, 1983), pp. 287–302.

A. Gelfand and A. Smith, “Sampling-based approaches to calculating marginal densities,” J. Am. Stat. Assoc. 85, 398–409 (1990).

[CrossRef]

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

W. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika 57, 97–109 (1970).

[CrossRef]

M. Wiper, D. Insua, and F. Ruggeri, “Mixtures of gamma distributions with applications,” J. Comput. Graph. Stat. 10, 440–454 (2001).

[CrossRef]

H. Ishwaran and M. Zarepour, “Exact and approximate sum representations for the Dirichlet process,” Can. J. Stat. 30, 269–283 (2002).

[CrossRef]

E. Jakeman and P. Pusey, “Significance of the k-distribution in scattering experiments,” Phys. Rev. Lett. 40, 546–550 (1978).

[CrossRef]

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003).

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

O. Korotkova, S. Avramov-Zamurovic, R. Malek-Madani, and C. Nelson, “Probability density function of the intensity of a laser beam propagating the maritime environment,” Opt. Express 19, 20322–20331 (2011).

[CrossRef]

A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39, 1–38 (1977).

E. Wang, D. Liu, J. Silva, and L. Carin, “Joint analysis of time-evolving binary matrices and associated documents,” in Advances in Neural Information Processing Systems (Curran Associates, 2010), pp. 2370–2378.

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

O. Korotkova, S. Avramov-Zamurovic, R. Malek-Madani, and C. Nelson, “Probability density function of the intensity of a laser beam propagating the maritime environment,” Opt. Express 19, 20322–20331 (2011).

[CrossRef]

J. Marin, K. Mengersen, and C. Roberts, Handbook of Statistics: Bayesian Thinking - Modeling and Computation (Elsevier, 2011), Chap. 25.

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

J. Marin, K. Mengersen, and C. Roberts, Handbook of Statistics: Bayesian Thinking - Modeling and Computation (Elsevier, 2011), Chap. 25.

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

O. Korotkova, S. Avramov-Zamurovic, R. Malek-Madani, and C. Nelson, “Probability density function of the intensity of a laser beam propagating the maritime environment,” Opt. Express 19, 20322–20331 (2011).

[CrossRef]

J. Neyman and E. Pearson, “On the problem of the most efficient tests of statistical hypothesis,” Philos. Trans. R. Soc. London 231, 289–337 (1933).

[CrossRef]

D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003).

P. Orbanz and J. Buhmann, “Smooth image segmentation by nonparametric Bayesian inference,” in European Conference on Computer Vision (Springer, 2006), Vol. 1, pp. 444–457.

P. Orbanz and Y. Teh, “Bayesian nonparametric models,” in Encyclopedia of Machine Learning (Springer, 2010), pp. 81–89.

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Sci. 33, 1065–1076 (1962).

[CrossRef]

J. Neyman and E. Pearson, “On the problem of the most efficient tests of statistical hypothesis,” Philos. Trans. R. Soc. London 231, 289–337 (1933).

[CrossRef]

D. Dunson and N. Pillai, “Bayesian density regression,” J. R. Stat. Soc. 69, 163–183 (2007).

[CrossRef]

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

E. Jakeman and P. Pusey, “Significance of the k-distribution in scattering experiments,” Phys. Rev. Lett. 40, 546–550 (1978).

[CrossRef]

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

J. Marin, K. Mengersen, and C. Roberts, Handbook of Statistics: Bayesian Thinking - Modeling and Computation (Elsevier, 2011), Chap. 25.

M. Rosenblatt, “Remarks on some nonparametric estimates of density function,” Ann. Math. Sci. 27, 832–837 (1956).

[CrossRef]

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

M. Aitkin and D. Rubin, “Estimation and hypothesis testing in finite mixture models,” J. R. Stat. Soc. 47, 67–75 (1985).

A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39, 1–38 (1977).

M. Wiper, D. Insua, and F. Ruggeri, “Mixtures of gamma distributions with applications,” J. Comput. Graph. Stat. 10, 440–454 (2001).

[CrossRef]

J. Sethuraman, “A constructive definition of Dirichlet priors,” Statistica Sinica 4, 639–650 (1994).

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

E. Wang, D. Liu, J. Silva, and L. Carin, “Joint analysis of time-evolving binary matrices and associated documents,” in Advances in Neural Information Processing Systems (Curran Associates, 2010), pp. 2370–2378.

B. Silverman, “Survey of existing methods,” in Density Estimates for Statistics and Data Analysis, Monographs on Statistics and Applied Probability (Chapman & Hall, 1986), pp. 1–22.

A. Gelfand and A. Smith, “Sampling-based approaches to calculating marginal densities,” J. Am. Stat. Assoc. 85, 398–409 (1990).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

W. Strohbein, T. Wang, and J. Speck, “On the probability distribution of line-of-sight fluctuations for optical signals,” Radio Sci. 10, 59–70 (1975).

W. Strohbein, T. Wang, and J. Speck, “On the probability distribution of line-of-sight fluctuations for optical signals,” Radio Sci. 10, 59–70 (1975).

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

P. Orbanz and Y. Teh, “Bayesian nonparametric models,” in Encyclopedia of Machine Learning (Springer, 2010), pp. 81–89.

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

Q. An, C. Wang, I. Shterev, E. Wang, L. Carin, and D. Dunson, “Hierarchical kernel stick-breaking process for multi-task image analysis,” in International Conference on Machine Learning (ICML) (Omnipress, 2008), pp. 17–24.

E. Wang, D. Liu, J. Silva, and L. Carin, “Joint analysis of time-evolving binary matrices and associated documents,” in Advances in Neural Information Processing Systems (Curran Associates, 2010), pp. 2370–2378.

W. Strohbein, T. Wang, and J. Speck, “On the probability distribution of line-of-sight fluctuations for optical signals,” Radio Sci. 10, 59–70 (1975).

K. Corsey and A. Webb, “Bayesian gamma mixture model approach to radar target recognition,” IEEE Trans. Aerosp. Electron. Syst. 39, 1201–1217 (2003).

A. Webb, “Gamma mixture models for target recognition,” Pattern Recogn. 33, 2045–2054 (2000).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

M. Welling, “Robust series expansions for probability density estimation,” Technical Note (Department of Electrical and Computer Engineering, California Institute of Technology, 2001).

M. Escobar and M. West, “Bayesian density estimation and inference using mixtures,” J. Am. Stat. Assoc. 90, 577–588 (1995).

[CrossRef]

M. Wiper, D. Insua, and F. Ruggeri, “Mixtures of gamma distributions with applications,” J. Comput. Graph. Stat. 10, 440–454 (2001).

[CrossRef]

H. Ishwaran and M. Zarepour, “Exact and approximate sum representations for the Dirichlet process,” Can. J. Stat. 30, 269–283 (2002).

[CrossRef]

M. Rosenblatt, “Remarks on some nonparametric estimates of density function,” Ann. Math. Sci. 27, 832–837 (1956).

[CrossRef]

E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Sci. 33, 1065–1076 (1962).

[CrossRef]

W. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika 57, 97–109 (1970).

[CrossRef]

H. Ishwaran and M. Zarepour, “Exact and approximate sum representations for the Dirichlet process,” Can. J. Stat. 30, 269–283 (2002).

[CrossRef]

K. Corsey and A. Webb, “Bayesian gamma mixture model approach to radar target recognition,” IEEE Trans. Aerosp. Electron. Syst. 39, 1201–1217 (2003).

I. Pruteanu-Malcini, L. Ren, J. Paisley, E. Wang, and L. Carin, “Hierarchical Bayesian modeling of topics in time-stamped documents,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 996–1011 (2010).

[CrossRef]

A. Gelfand and A. Smith, “Sampling-based approaches to calculating marginal densities,” J. Am. Stat. Assoc. 85, 398–409 (1990).

[CrossRef]

Y. Teh, M. Jordan, M. Beal, and M. Jordan, “Hierarchical Dirichlet processes,” J. Am. Stat. Assoc. 101, 1566–1581 (2005).

[CrossRef]

M. Escobar and M. West, “Bayesian density estimation and inference using mixtures,” J. Am. Stat. Assoc. 90, 577–588 (1995).

[CrossRef]

J. McLaren, J. Thomas, J. Mackintosh, K. Mudge, K. Grant, B. Clare, and W. Cowley, “Comparison of probability density functions for analyzing irradiance statistics due to atmospheric turbulence,” J. Appl. Opt. 51, 5996–6002 (2012).

[CrossRef]

N. Metropolis, A. Rosenbluth, M. Rosenbluth, and A. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1092 (1953).

[CrossRef]

M. Wiper, D. Insua, and F. Ruggeri, “Mixtures of gamma distributions with applications,” J. Comput. Graph. Stat. 10, 440–454 (2001).

[CrossRef]

D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003).

R. Barakat, “Second-order statistics of integrated intensities and detected photons, the exact analysis,” J. Mod. Opt. 43, 1237–1252 (1996).

[CrossRef]

M. Al-Habash, L. Andrews, and R. Phillips, “Mathematical model for the irradiance probability density function of a laser beam propagating through turbulent media,” J. Opt. Eng. 40, 1554–1562 (2001).

[CrossRef]

D. Mudge, A. Wedd, J. Craig, and J. Thomas, “Statistical measurements of irradiance fluctuations produced by a reflective membrane optical scintillator,” J. Opt. Laser Technol. 28, 381–387 (1996).

[CrossRef]

A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39, 1–38 (1977).

M. Aitkin and D. Rubin, “Estimation and hypothesis testing in finite mixture models,” J. R. Stat. Soc. 47, 67–75 (1985).

D. Dunson and N. Pillai, “Bayesian density regression,” J. R. Stat. Soc. 69, 163–183 (2007).

[CrossRef]

C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan, “An introduction to MCMC for machine learning,” Mach. Learn. 50, 5–43 (2003).

[CrossRef]

O. Korotkova, S. Avramov-Zamurovic, R. Malek-Madani, and C. Nelson, “Probability density function of the intensity of a laser beam propagating the maritime environment,” Opt. Express 19, 20322–20331 (2011).

[CrossRef]

Y. Jiang, J. Ma, L. Tan, S. Yu, and W. Du, “Measurement of optical intensity fluctuation over an 11.8 km turbulent path,” Opt. Express 16, 6963–6973 (2008).

[CrossRef]

A. Webb, “Gamma mixture models for target recognition,” Pattern Recogn. 33, 2045–2054 (2000).

[CrossRef]

J. Neyman and E. Pearson, “On the problem of the most efficient tests of statistical hypothesis,” Philos. Trans. R. Soc. London 231, 289–337 (1933).

[CrossRef]

E. Jakeman and P. Pusey, “Significance of the k-distribution in scattering experiments,” Phys. Rev. Lett. 40, 546–550 (1978).

[CrossRef]

C. Nelson, S. Avramov-Zamurovic, R. Malek-Madani, O. Korotkova, R. Sova, and F. Davidson, “Measurements and comparison of the probability density and covariance functions of laser beam intensity fluctuations in a hot-air turbulence emulator with the maritime atmospheric environment,” Proc. SPIE 8517, 851707 (2012).

W. Strohbein, T. Wang, and J. Speck, “On the probability distribution of line-of-sight fluctuations for optical signals,” Radio Sci. 10, 59–70 (1975).

J. Sethuraman, “A constructive definition of Dirichlet priors,” Statistica Sinica 4, 639–650 (1994).

E. Wang, D. Liu, J. Silva, and L. Carin, “Joint analysis of time-evolving binary matrices and associated documents,” in Advances in Neural Information Processing Systems (Curran Associates, 2010), pp. 2370–2378.

T. Ferguson, “Bayesian density estimation by mixtures of normal distributions,” Recent Advances in Statistics (Academic, 1983), pp. 287–302.

P. Orbanz and J. Buhmann, “Smooth image segmentation by nonparametric Bayesian inference,” in European Conference on Computer Vision (Springer, 2006), Vol. 1, pp. 444–457.

M. J. Beal, “Variational algorithms for approximate Bayesian inference,” Ph.D. Thesis (University College London, 2003).

J. Marin, K. Mengersen, and C. Roberts, Handbook of Statistics: Bayesian Thinking - Modeling and Computation (Elsevier, 2011), Chap. 25.

P. Orbanz and Y. Teh, “Bayesian nonparametric models,” in Encyclopedia of Machine Learning (Springer, 2010), pp. 81–89.

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