  This paper considers the problem of sequential fusion of predictions from
neural networks (NN) and fusion of predictions from multiple NN. This fusion
strategy increases the robustness, i.e., reduces the impact of one incorrect
classification and detection of outliers the \nn has not seen during training.
This paper uses Laplacian approximation of Bayesian NNs (BNNs) to quantify the
uncertainty necessary for fusion. Here, an extension is proposed such that the
prediction of the NN can be represented by multimodal distributions. Regarding
calibration of the estimated uncertainty in the prediction, the performance is
significantly improved by having the flexibility to represent a multimodal
distribution. Two class classical image classification tasks, i.e., MNIST and
CFAR10, and image sequences from camera traps of carnivores in Swedish forests
have been used to demonstrate the fusion strategies and proposed extension to
the Laplacian approximation.
