I'll be presenting on work in progress in collaboration with Bijan Pesaran's group (Yan Wong, Mariana Vigeral, David Putrino) and Josh Merel on building a high degree-of-freedom brain-machine interface. I'll focus on the Bayesian paradigm for decoding, and two practical problems for pushing that paradigm beyond the commonly-used Kalman filtering approach: building better likelihoods, and building better priors. The first amounts to fitting tuning curves for various neurons. Other groups have shown a nonlinear dependence of firing rate on hand position in 3D space, here I will show some preliminary results on fitting tuning curves for large numbers of joint angles. The second amounts to building better generative models of reach and grasp motions. As a first step in that direction, I've looked at PCA and ICA for reducing the dimension of reach-and-grasp signals.