I’ve worked on a variety of topics, but moving forward I’m primarily interested in:
- combining genetic algorithms with variants of simulated annealing
- general applied statistics and machine learning
- unifying and efficiently organizing mathematical statistical knowledge
- revealing visual or intuitive ways of understanding concepts
- W. D. Brinda, Jason Klusowski, and Dana Yang. Hölder’s identity. Statistics and Probability Letters, 2019.
- Jason Klusowski, Dana Yang, and W. D. Brinda. Estimating the coefficients of a mixture of two linear regressions by expectation maximization. IEEE Transactions on Information Theory, 2019
- W. D. Brinda. Adaptive Estimation with Gaussian Radial Basis Mixtures. PhD thesis, Yale University, 2018. (pdf | code)
- W. D. Brinda and Jason M. Klusowski. Finite-sample risk bounds for maximum likelihood estimation with arbitrary penalties. IEEE Transactions on Information Theory, 2018.
- W. D. Brinda, Shantanu Jain, and Michael Trosset. Inference on random graphs with classified edge attributes. Indiana University Department of Statistics Technical Report 11-03, 2011.