We present a further development of a method for accelerating the calculation
of CMB power spectra, matter power spectra and likelihood functions for use in
cosmological Bayesian inference. The algorithm, called {\sc CosmoNet}, is based
on training a multilayer perceptron neural network. We compute CMB power
spectra (up to â„“=2000) and matter transfer functions over a hypercube in
parameter space encompassing the 4σ confidence region of a selection of
CMB (WMAP + high resolution experiments) and large scale structure surveys (2dF
and SDSS). We work in the framework of a generic 7 parameter non-flat
cosmology. Additionally we use {\sc CosmoNet} to compute the WMAP 3-year, 2dF
and SDSS likelihoods over the same region. We find that the average error in
the power spectra is typically well below cosmic variance for spectra, and
experimental likelihoods calculated to within a fraction of a log unit. We
demonstrate that marginalised posteriors generated with {\sc CosmoNet} spectra
agree to within a few percent of those generated by {\sc CAMB} parallelised
over 4 CPUs, but are obtained 2-3 times faster on just a \emph{single}
processor. Furthermore posteriors generated directly via {\sc CosmoNet}
likelihoods can be obtained in less than 30 minutes on a single processor,
corresponding to a speed up of a factor of ∼32. We also demonstrate the
capabilities of {\sc CosmoNet} by extending the CMB power spectra and matter
transfer function training to a more generic 10 parameter cosmological model,
including tensor modes, a varying equation of state of dark energy and massive
neutrinos. {\sc CosmoNet} and interfaces to both {\sc CosmoMC} and {\sc
Bayesys} are publically available at {\tt
www.mrao.cam.ac.uk/software/cosmonet}.Comment: 8 pages, submitted to MNRA