MoM function

Mixture of Multinomial Model Prior description class

An S4 class to represent a Mixture of Multinomial model. Such model can be used to cluster a data matrix $X$ with the following generative model : [REMOVE_ME]$\pi \sim Dirichlet(\alpha) [REMOVE_ME_2]$

[REMOVE_ME]$Z_i \sim \mathcal{M}(1,\pi) [REMOVE_ME_2]$

[REMOVE_ME]$\theta_{k} \sim Dirichlet(\beta) [REMOVE_ME_2]$

[REMOVE_ME]$X_{i.}|Z_{ik}=1 \sim \mathcal{M}(L_i,\theta_{k}) [REMOVE_ME_2]$

With $L_i=\sum_d=1^DX_{id}$. These classes mainly store the prior parameters value ($\alpha,\beta$) of this generative model. The MoM-class must be used when fitting a simple Mixture of Multinomials whereas the MoMPrior-class must be sued when fitting a CombinedModels-class. class

MoMPrior(beta = 1)

MoM(alpha = 1, beta = 1)

Arguments

• beta: Dirichlet over vocabulary prior parameter (default to 1)
• alpha: Dirichlet prior parameter over the cluster proportions (default to 1)

Returns

a MoMPrior-class object

a MoM-class object

Description

An S4 class to represent a Mixture of Multinomial model. Such model can be used to cluster a data matrix $X$ with the following generative model :

With $L_i=\sum_d=1^DX_{id}$. These classes mainly store the prior parameters value ($\alpha,\beta$) of this generative model. The MoM-class must be used when fitting a simple Mixture of Multinomials whereas the MoMPrior-class must be sued when fitting a CombinedModels-class.

Examples

MoMPrior()
MoMPrior(beta = 0.5)
MoM()
MoM(beta = 0.5)

See Also

MoMFit-class, MoMPath-class

Other DlvmModels: CombinedModels, DcLbm, DcSbm, DiagGmm, DlvmPrior-class, Gmm, Lca, MoR, MultSbm, Sbm, greed()