Bayesian Neural Network
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In this problem, we will implement a Bayesian neural network to estimate the uncertainty of its predictions. We will use a Bayesian approach to quantify the uncertainty.
**Example:** Suppose we have a simple neural network with one hidden layer and we want to estimate the uncertainty of its predictions. We can use a Bayesian neural network to do this.
**Constraints:** You can assume that the data is normally distributed and that the model is a simple neural network with one hidden layer.
Test Cases
Test Case 1
Input:
[[1, 2], [3, 4], [5, 6]]Expected:
[[3.0, 0.0], [7.0, 0.0], [11.0, 0.0]]Test Case 2
Input:
[[10, 20], [30, 40], [50, 60]]Expected:
[[35.0, 0.0], [75.0, 0.0], [115.0, 0.0]]+ 3 hidden test cases