nQ: E-step function Q
In RestoreNet: Random-Effects Stochastic Reaction Networks

nQ	R Documentation

E-step function Q

Negative E-step function -Q of the expectation-maximization algorithm

nQ(theta, euy_curr, vuy_curr, M, M_bdiag, y, V, VCNs, nObs, dW)

`theta`	p-dimensional vector parameter.
`euy_curr`	current value of the conditional expectation `E[u \vert y]` of u given y, where u and y are the latent and observed states respectively.
`vuy_curr`	current value of the conditional variance `V[u \vert y]` of u given y, where u and y are the latent and observed states respectively.
`M`	A `n \times K` dimensional (design) matrix.
`M_bdiag`	A`n \times Jp` dimensional block-diagonal design matrix. Each j-th block (`j = 1,\dots,J`) is a `n_j \times p` dimensional design matrix for the j-th clone.
`y`	n-dimensional vector of the time-adjacent cellular increments
`V`	A `p \times K` dimensional net-effect matrix.
`VCNs`	A n-dimensional vector including values of the vector copy number corresponding to the cell counts of y.
`nObs`	A K-dimensional vector including the frequencies of each clone k (`k = 1,\dots,K`).
`dW`	p-dimensional list of the partial derivatives of W w.r.t. theta.