Better guesses for initial MCMC walker position. In detail:
  * Single stars: guess E(B-V), given M_r and [Fe/H], using
                  color excess of observed - model colors.
  * Line of sight: When disk-like dust prior is turned on,
                   draw guesses from disk-like priors.
Guess distances for single stars based on difference between model mags and apparent mags.
Working on l.o.s. priors.
Fix bug in guessing E(B-V). Abort if no sane guesses found.
Line-of-sight priors:
  * Index of power spectrum: -2.5
  * Variance: 1.5 orders of magnitude
  * Normalizing reddening out to 1 kpc to 0.2 mags in E(B-V)
  * Including bias to mean of log(Delta A)
Turning switches of adjacent log(Delta A) values back on
Changing non-disk-like prior to -4 +- 2
Updating walkers in affine sampler immediately after each proposal.
Fixing guesses for individual stars:
  * Taking reddening into account when picking DM
query_lsd.py uses magsqx
Changing completeness to use Delta m_1 = 0.1
Merge branch 'better-guess' of github.com:gregreen/bayestar into better-guess
Introducing unbalanced steps into burn-in to remove spurious modes
Using more non-ergodic replacement steps to remove spurious modes for single stars.
Option to weaken reversibility in affine sampler slightly.
  * Adds small factor epsilon into the reversibility factor:
      Q(Y->X) / Q(X->Y) + epsilon
  * epsilon set using set_replacement_accept_bias(double epsilon)
Decreasing ergodicity in burn-in for individual stars
Weighting samples by likelihoods when determining covariance for
draws. This is similar in spirit to calculating a clipped covariance.
Tweaking single-star run parameters to remove spurious modes
more effectively:
  * Using biased replacement steps in burn-in.
  * Weighting walkers by likelihoods when calculating proposal covariance.
Fixing initial guesses for individual stars in difficult cases.
  * Use all colors to estimate reddening, instead of only one
    color. Weight each color by inverse sigma^2.
  * Do not seed guesses to have predicted apparent magnitudes
    beyond magnitude limit. Adjust DM guess to achieve this.
Fixed bug in calculation of weighted ensemble covariance that caused
covariance to collapse to zero volume. This bug came up when ln(p) was
very low for all samples. The fix involved shifting ln(p)'s in
calculation by ln(p_max).
Line-of-sight sampler initial starting points:
  * Vector added to guess has negative covariance between neighboring
    Deltas.
  * Initial starting points have same reddening at infinity as
    guess, up to small log factor.