Finite mixtures of multivariate Poisson distributions with application

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A bivariate INAR(1) process with application (2011, methodology): “…literature, as, e.g., the bivariate Poisson lognormal model of Aitchinson and Ho (1989) (see also Chib and Winkelmann, 2001), the finite mixture model developed by Karlis and Meligkotsidou (2007) and models based on copulas see, e.g., Nikoloulopoulos and Karlis (2009) and the references therein.”
Count Time Series: A Methodological Review (2021, methodology): “The probability mass function of a finite mixture of L multivariate Poisson distributions has the representation (Karlis and Meligkotsidou 2007) P(x|μ, π) = L∑ =1 π MPd(x|μ ), where π = (π1, . . . , πL)T book keeps mixture proportions.”
A mixture of generalized hyperbolic distributions (2013, background): “…(Peel and McLachlan, 2000), the skew-normal distribution (Lin, 2009), the skew-t distribution (Lin, 2010; Lee and McLachlan, 2011; Vrbik and McNicholas, 2012; Murray et al., 2013), as well as other approaches (Karlis and Meligkotsidou, 2007; Handcock et al., 2007; Browne et al., 2012).”