minor speedups with some new numerical code

I added `logdet_low_rank2` and `quad_form_diag_plus_lr2` which give some minor speed improvements (but remain the bottleneck).

Here is a profile snippet using the code before this PR:

```
   260    160000      1051426      6.6      1.1                  from utils.utils import invert_low_rank, det_low_rank, logdet_low_rank, quad_form_diag_plus_lr
   261                                                           # Ainv = 1.0/np.asscalar(self.sigma) * np.eye(N)
   262    160000      2381164     14.9      2.5                  d = np.asscalar(self.sigma) * np.ones((N,))
   263    160000      1004804      6.3      1.1                  Ainv = 1.0/d
   264                                           
   265                                                           # Sig_marg_inv = invert_low_rank(Ainv, X, self.Sigma_A, X.T, diag=True)
   266    160000       962476      6.0      1.0                  yy = y-mu_marg
   267    160000     47260305    295.4     49.5                  tmp = -1./2. * quad_form_diag_plus_lr(yy, d, X, self.Sigma_w, X.T)
   268                                                           out = tmp \
   269                                                                 - N/2*np.log(2*np.pi) \
   270    160000     37701745    235.6     39.5                        - 0.5 * logdet_low_rank(Ainv, X, self.Sigma_w, X.T, diag=True)
```

Here's a profile snippet of the same section (using the same seed) after this PR:

```
   260    160000       921334      5.8      1.6                  from utils.utils import invert_low_rank, det_low_rank, logdet_low_rank2, quad_form_diag_plus_lr2
   261                                                           # Ainv = 1.0/np.asscalar(self.sigma) * np.eye(N)
   262    160000      2072269     13.0      3.6                  d = np.asscalar(self.sigma) * np.ones((N,))
   263    160000       904181      5.7      1.6                  Ainv = 1.0/d
   264                                           
   265                                                           # Sig_marg_inv = invert_low_rank(Ainv, X, self.Sigma_A, X.T, diag=True)
   266    160000       878196      5.5      1.5                  yy = y-mu_marg
   267    160000     25023198    156.4     43.6                  tmp = -1./2. * quad_form_diag_plus_lr2(yy, d, X, self.Sigma_w, X.T)
   268                                                           out = tmp \
   269                                                                 - N/2*np.log(2*np.pi) \
   270    160000     22805308    142.5     39.7                        - 0.5 * logdet_low_rank2(Ainv, X, self.Sigma_w, X.T, diag=True)
```

So the total times on those two lines go from 47260305 to 25023198 and from 37701745 to 22805308, respectively. On my laptop, the overall iteration time for the HMM demo using some made-up synthetic data parameters (N=40, Nstates=20) improved about 30% (from 44sec/iter to 32sec/iter).

The function `resample_As_and_regression_models` remains the bottleneck.