G k is generally assumed to be a known matrix in n × n dimension.
where 0 ≤ w ≤ 1, S is a known matrix.
Since both the exposure setting and the emission waveform are known, the matrix G L is a known matrix.
A, D, B are known matrices.
where is an observable random matrix,,, and are known matrices, respectively., are unknown matrices.
C ∈ R m × n is a known constant matrix.
Two different models of the channel will be considered: (i) A deterministic model (ii) A stochastic Rayleigh fading modelb, i.e., vec ( H ) ∈ C N ( 0, R ), where, for mathematical tractability, we will assume that the known covariance matrix R possesses the Kronecker model used, e.g., in [7, 10]: R = R T T ⊗ R R (1) .
A stochastic Rayleigh fading modelb, i.e., vec ( H ) ∈ C N ( 0, R ), where, for mathematical tractability, we will assume that the known covariance matrix R possesses the Kronecker model used, e.g., in [7, 10]: R = R T T ⊗ R R (1).
In what follows we assume η is a known parameter.
Furthermore, we assume that the control inputs are injected directly into separate compartments so that and in (4.14) are such that is a positive diagonal matrix and, where,, is a known positive diagonal matrix function.
Briefly, the MNE method finds the maximum a posteriori estimates of the latent cortical sources, given the observed sensor sources, assuming (1) the current source amplitudes are sparse and normally distributed with a known source covariance matrix and (2) the observed sensor data contain additive noise with a normal distribution and a known spatial covariance matrix.
