Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation Appendix A Lemma and Proof

For the comprehensiveness of proof, we duplicate Lemma 3.1 here. If we use Lemma A.1 with diagonal covariance matrices for In this section, we outline additional details of the experimental settings including the datasets (Appendix B.1), hyperparameters of the models used (Appendix B.2), metrics (Appendix B.3), and a brief analysis of computational complexity of MGP and MNPs (Appendix B.4). We generated 1,000 synthetic training samples (i.e., Robustness to Noisy Samples Dataset In Section 5.1, we evaluated the models' robustness to The details of each dataset are outlined in Table 1. These datasets lie within a feature space where each feature extraction method can be found in [5]. Table 1: Multimodal datasets used for evaluating robustness to noisy samples.