Following the blog post, where the dropout in the deep learning models has been approximated to a Gaussian process. Research paper and its appendix.Looking at the suggestion of the author from the blog post that the variance in the prediction is given by the following piece of code. I found it counterintuitive as the predictive variance is directly proportional to the number of observations. tau -- which author refers as model precision.I have tried to go through the research paper but it seems I don't have enough skills to comprehend the reasoning behind it.Shouldn't the variance decrease with the more observations?probs = []for _ in xrange(T):    probs += [model.output_probs(input_x)]predictive_mean = numpy.mean(prob, axis=0)predictive_variance = numpy.var(prob, axis=0)tau = l**2 * (1 - model.p) / (2 * N * model.weight_decay)predictive_variance += tau**-1 
Clarification in model precision term for Gaussian Processes
machine learning;neural networks