bayesian

Measuring incertainty in Bayesian Neural Network Question: Hy everybody, I’m beginning with tensorflow probability and I have some difficulties to interpret my Bayesian neural network outputs. I’m working on a regression case, and started with the example provided by tensorflow notebook here: https://blog.tensorflow.org/2019/03/regression-with-probabilistic-layers-in.html?hl=fr As I seek to know the uncertainty of my network predictions, I …

Gaussian Process Regression: standard deviation meaning Question: In the following code about the Gaussian Process Regression (GPR): from sklearn.datasets import make_friedman2 from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel X, y = make_friedman2(n_samples=500, noise=0, random_state=0) kernel = DotProduct() + WhiteKernel() gpr = GaussianProcessRegressor(kernel=kernel, random_state=0).fit(X, y) print gpr.score(X, y) print gpr.predict(X[:2,:], return_std=True) What is the …

pythonic implementation of Bayesian networks for a specific application Question: This is why I’m asking this question: Last year I made some C++ code to compute posterior probabilities for a particular type of model (described by a Bayesian network). The model worked pretty well and some other people started to use my software. Now I …