  In this paper we propose a novel Bayesian solution for nonlinear regression
in complex fields. Previous solutions for kernels methods usually assume a
complexification approach, where the real-valued kernel is replaced by a
complex-valued one. This approach is limited. Based on results in
complex-valued linear theory and Gaussian random processes we show that a
pseudo-kernel must be included. This is the starting point to develop the new
complex-valued formulation for Gaussian process for regression (CGPR). We face
the design of the covariance and pseudo-covariance based on a convolution
approach and for several scenarios. Just in the particular case where the
outputs are proper, the pseudo-kernel cancels. Also, the hyperparameters of the
covariance {can be learnt} maximizing the marginal likelihood using Wirtinger's
calculus and patterned complex-valued matrix derivatives. In the experiments
included, we show how CGPR successfully solve systems where real and imaginary
parts are correlated. Besides, we successfully solve the nonlinear channel
equalization problem by developing a recursive solution with basis removal. We
report remarkable improvements compared to previous solutions: a 2-4 dB
reduction of the MSE with {just a quarter} of the training samples used by
previous approaches.
