Tag: python

  • Gaussian Processes: Harnessing the Bayesian Paradigm

    test Gaussian Processes (GPs) stand as an embodiment of Bayesian principles in the realm of machine learning and probabilistic modeling. Today we begin a mathematical voyage to show the integration of Gaussian Process Regression within the Bayesian framework. This exploration will unravel the inherent connections between Gaussian Processes and Bayes’ theorem, delve into the complexities…

  • Gaussian Processes: An Intro to Nonparametric Regression

    test Introduction In the world of machine learning, finding models that balance precision and flexibility is essential. Gaussian Processes (GPs) offer a captivating approach that thrives in the realm of nonparametric regression. With GPs, we venture beyond fixed assumptions, allowing uncertainty and adaptability to shine in our predictions. My goal here is to make this…

  • Sparse Matrices in Numba

    Welcome file Introduction What is a sparse matrix? Creating a sparse matrix Adding and subtracting sparse matrices Multiplying sparse matrices Multiply matrix and vector The dot function Summing over a dimension Wall time comparison Using smn within a Numba compiled function Conclusion Introduction Numba is a widely-used just-in-time (JIT) compiler for Python that optimizes the…