GPstuff 4.6 released plus github changes

[Aki Vehtari]

GPstuff 4.6 is now available from
http://becs.aalto.fi/en/research/bayes/gpstuff/
and
http://mloss.org/software/view/451/
and
https://github.com/gpstuff-dev/gpstuff

Github has now also development branch (develop) visible. Branch master has the
latest release. Notice that the github address has changed since BECS
depertament ceased to exist.

2015-07-09 Version 4.6

New features

- Use Pareto smoothed importance sampling (Vehtari & Gelman, 2015) for
- importance sampling leave-one-out cross-validation
(gpmc_loopred.m)
- importance sampling integration over hyperparameters
(gp_ia.m)
- importance sampling part of the logistic Gaussian process density
estimation (lgpdens.m)
- references:
- Aki Vehtari and Andrew Gelman (2015). Pareto smoothed importance
sampling. arXiv preprint arXiv:1507.02646.
- Aki Vehtari, Andrew Gelman and Jonah Gabry (2015). Efficient
implementation of leave-one-out cross-validation and WAIC for
evaluating fitted Bayesian models.

- New covariance functions
- gpcf_additive creates a mixture over products of kernels for each dimension
reference: Duvenaud, D. K., Nickisch, H., & Rasmussen, C. E. (2011).
Additive Gaussian processes. In Advances in neural information
processing systems, pp. 226-234.
- gpcf_linearLogistic corresponds to logistic mean function
- gpcf_linearMichelismenten correpsonds Michelis Menten mean function

Improvements
- faster EP moment calculation for lik_logit

Several minor bugfixes