Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model .
We use recent cosmological observations and Bayesian model averaging to derive tight limits on the curvature parameter , as well as robust lower bounds on the curvature radius of the Universe and its minimum size , while allowing for the possibility of an evolving dark energy component .
Because flat models are favoured by Bayesian model selection , we find that model-averaged constraints on the curvature and size of the Universe can be considerably stronger than non model-averaged ones .
For the most conservative prior choice ( based on inflationary considerations ) , our procedure improves on non model-averaged constraints on the curvature by a factor of \sim 2 .
The curvature scale of the Universe is conservatively constrained to be R _ { c } > 42 Gpc ( 99 \% ) , corresponding to a lower limit to the number of Hubble spheres in the Universe N _ { U } > 251 ( 99 \% ) .