bmixture-package

Bayesian Estimation for Finite Mixture of Distributions

The R package bmixture provides statistical tools for Bayesian estimation in finite mixture of distributions. The package implemented the improvements in the Bayesian literature, including Mohammadi and Salehi-Rad (2012) and Mohammadi et al. (2013). Besides, the package contains several functions for simulation and visualization, as well as a real dataset taken from the literature. package

How to cite this package

Whenever using this package, please cite as

Mohammadi R. (2019). bmixture: Bayesian Estimation for Finite Mixture of Distributions, R package version 1.5, https://CRAN.R-project.org/package=bmixture

Author(s)

Reza Mohammadi a.mohammadi@uva.nl

References

Mohammadi, A., Salehi-Rad, M. R., and Wit, E. C. (2013) Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service. Computational Statistics, 28(2):683-700, tools:::Rd_expr_doi("10.1007/s00180-012-0323-3")

Mohammadi, A., and Salehi-Rad, M. R. (2012) Bayesian inference and prediction in an M/G/1 with optional second service. Communications in Statistics-Simulation and Computation, 41(3):419-435, tools:::Rd_expr_doi("10.1080/03610918.2011.588358")

Stephens, M. (2000) Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods. Annals of statistics, 28(1):40-74, tools:::Rd_expr_doi("10.1214/aos/1016120364")

Richardson, S. and Green, P. J. (1997) On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: series B, 59(4):731-792, tools:::Rd_expr_doi("10.1111/1467-9868.00095")

Green, P. J. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711-732, tools:::Rd_expr_doi("10.1093/biomet/82.4.711")

Cappe, O., Christian P. R., and Tobias, R. (2003) Reversible jump, birth and death and more general continuous time Markov chain Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 65(3):679-700

Wade, S. and Ghahramani, Z. (2018) Bayesian Cluster Analysis: Point Estimation and Credible Balls (with Discussion). Bayesian Analysis, 13(2):559-626, tools:::Rd_expr_doi("10.1214/17-BA1073")

Examples

## Not run:

require( bmixture )

data( galaxy )

# Runing bdmcmc algorithm for the galaxy dataset      
mcmc_sample = bmixnorm( data = galaxy )

summary( mcmc_sample ) 
plot( mcmc_sample )
print( mcmc_sample)

# simulating data from mixture of Normal with 3 components
n      = 500
mean   = c( 0  , 10 , 3   )
sd     = c( 1  , 1  , 1   )
weight = c( 0.3, 0.5, 0.2 )
    
data = rmixnorm( n = n, weight = weight, mean = mean, sd = sd )
   
# plot for simulation data      
hist( data, prob = TRUE, nclass = 30, col = "gray" )
    
x           = seq( -20, 20, 0.05 )
densmixnorm = dmixnorm( x, weight, mean, sd )
      
lines( x, densmixnorm, lwd = 2 )  
     
# Runing bdmcmc algorithm for the above simulation data set      
bmixnorm.obj = bmixnorm( data, k = 3, iter = 1000 )
    
summary( bmixnorm.obj ) 
## End(Not run)