Posterior distribution of mixture models In the context of mixture models in Bayesian inference, one can assume that the general form of the joint posterior for a mixture model of $k$ components is 
$$
\begin{equation}
p( \boldsymbol{\lambda} , \boldsymbol{\theta}  \mid \boldsymbol{y}) \propto \bigg[ \prod_{i=1}^{n} \sum_{j=1}^{k} \lambda_{j} f(y_{i} \mid  \boldsymbol{\theta}_{j} )      \bigg] \ p(\boldsymbol{\lambda} ,\boldsymbol{\theta} ) \ .
\end{equation}
$$
Incorporating information about the missing variable $Z$ and thus using the augmented likelihood, the posterior accounting for the complete data structure reads (this formula is given in the excellent book "Bayesian Core" by Marin and Robert (2007))
$$
\begin{equation}
p(\boldsymbol{\theta}, \boldsymbol{\lambda} \mid \boldsymbol{y}, \boldsymbol{z})  \propto \bigg[     \prod_{i=1}^{n} \prod_{j=1}^{k} \lambda_{ j}^{z_{ij}} f (y_{i} \mid \boldsymbol{\theta}_{j})^{z_{ij}}      \bigg] \ \ p(\boldsymbol{\lambda} ,\boldsymbol{\theta} ) \ .
\end{equation}
$$
However, one can assume the following model decomposition for the joint posterior of all variables if we use independent prior for $\boldsymbol{\theta} = (\boldsymbol{\mu}, \boldsymbol{\sigma}^{2})$.
$$
 p(\boldsymbol{y}, \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2}) = p(\boldsymbol{y} \mid \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma^{2}}) \ p(\boldsymbol{z} \mid \boldsymbol{\lambda}) \ p(\boldsymbol{\lambda}) \ p(\boldsymbol{\mu}) \ p(\boldsymbol{\sigma^{2}}) \\
= p(\boldsymbol{y} \mid \boldsymbol{\Psi}) \ p(\boldsymbol{\Psi}) \ .\\
$$
where $\boldsymbol{\Psi} = (\boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma^{2}})$
I find it hard to see how the expression $p(\boldsymbol{\theta}, \boldsymbol{\lambda} \mid \boldsymbol{y}, \boldsymbol{z}) $ relates to $p(\boldsymbol{y}, \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2})$. Any hints ?
 A: Thank you for the appreciation of our book! By Bayes' formula, the posterior density
$$p(\boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2}|\boldsymbol{y}, \boldsymbol{z})$$
is proportional to the joint density
$$p(\boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2}|\boldsymbol{y}, \boldsymbol{z})\propto p(\boldsymbol{y}, \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2})$$
which itself decomposes into
$$p(\boldsymbol{y}, \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2}) = \overbrace{p(\boldsymbol{y} \mid \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma^{2}})}^\text{completed model} \ \overbrace{p(\boldsymbol{z} \mid \boldsymbol{\lambda})}^\text{latent model} \ \overbrace{p(\boldsymbol{\lambda}) \ p(\boldsymbol{\mu}) \ p(\boldsymbol{\sigma^{2}})}^\text{prior}
$$
and also
$$p(\boldsymbol{y}, \boldsymbol{z}, \boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2})= p(\boldsymbol{y}, \boldsymbol{z} \mid \boldsymbol{\Psi}) \ p(\boldsymbol{\Psi}) \ .\\$$
where $$\boldsymbol{\Psi}=(\boldsymbol{\lambda}, \boldsymbol{\mu}, \boldsymbol{\sigma}^{2})=(\boldsymbol{\lambda}, \boldsymbol{\theta})$$
(The $\boldsymbol{z}$ is missing in the second row of your equation.)
Note: The representation of the likelihood is much older than the book "Modèles à variables latentes et modèles de mélange" from Droesbeke, Saporta and Thomas-Agnan. See for instance Dempster, Laird and Rubin (1979).
