Implementing Conditional Independence and Understanding RCoT
Implemented the Conditional Independence using multiple ways: Using the cross-covariance operator and correlation coefficient Using the Hilbert Schmidt Norm Reference to Section 2: https://papers.nips.cc/paper/2007/file/3a0772443a0739141292a5429b952fe6-Paper.pdf Since the KCIT method for testing Conditional Dependence has disadvantages of the curse of dimensionality and time taken to process. A good approximation was done on KCIT to improve on the issues and hence RCoT was introduced. I have started to understand and get into the depth of it. Reference: https://arxiv.org/abs/1702.03877 Monday I had been assigned to review the RCot paper and understand the first 2 sections in it. Some notes I made during the review, I have noted below. Kernel independent testing is not time efficient and so it cannot be used for constraint-based conditional independence testing as the data sets in these settings are very huge. 2 options presented by the paper RCoT - Randomized conditional Correlations