With the end of the GSoC project, I will give this blog to summarise the JAX based optimization to analyze its applicability to enhance the loglikelihood calculation. The goal is to analyze, (i) the performance of different optimizers to evaluate the loglikelihood function, (ii) demonstrated the robustness of JAX to…

GSoC started four months ago and it is not just about knowing more about the open-source that made the experience great! My mentors made it way cooler than I thought it would be. I was writing my Master thesis, for the last three months and surely, it has been a…

In the last blog, I wrote about Introduction to JAX and Automatic Differentiation. In this one, my plan for the next stage of implementation. Currently, I am working on the modeling notebook (https://github.com/StingraySoftware/notebooks/blob/main/Modeling/ModelingExamples.ipynb) …

I assume everyone reading this is already aware of two classical forms of differentiation, namely symbolic and finite differentiation. Symbolic differentiation operates on expanded mathematical expressions which lead to inefficient code and introduction of truncation error while finite differentiation deals with round-off errors. Optimized calculation of derivatives is crucial when…