Figure 1.

Generative model: The figure shows the probabilistic dependencies underlying our generative model for fMRI data. The quantities in square brackets are constants and those in circles are random variables. The spatial regularisation coefficients α constrain the regression coefficients W. The parameters λ and A define the autoregressive error processes that contribute to the measurements. The spatial regularisation coefficients β constrain the AR coefficients A. The graph shows that the joint probability of parameters and data can be written p(Y, W, A, λ, α, β) = p(Y|W, A, λ)p(W|α)p(A|β)p(λ|u ₁, u ₂)p(α|q ₁, q ₂)p(β|r ₁, r ₂), where the first term is the likelihood and the other terms are the priors. The likelihood is given in Eq. (11) and the priors are defined in greater detail in Appendices APPENDIX A: PRECISIONS, APPENDIX B: REGRESSION COEFFICIENTS, APPENDIX C: AR COEFFICIENTS.