I learned a great deal throughout this journey. In the second part, I gained knowledge about implementing logistic regression in Stan. I also learned the significance of data type declarations for obtaining accurate estimates, how to use posterior to predict new data, and what generated quantities in Stan is for. Moreover, having a friend who is well-versed in Bayesian statistics proves invaluable when delving into the Bayesian realm! Very fun indeed!
There is a lot to learn about Bayesian statistics, but it’s fun, exciting, and flexible! I thoroughly enjoyed the beginning of this journey. There will be learning curves, but there are so many great people and resources out there to help us get closer to understanding the Bayesian way.
Sending key presses to another device using software that emulates a keyboard, but isn't a physical keyboard, is a fascinating concept. We understand that in the Linux/Unix environment and with Python, this can be accomplished through low-level programming. But can the R programming language achieve the same feat? If it can, then how does it work?
Interaction adventures through simulations and gradient boosting trees using the S-learner approach. I hadn’t realized that lightGBM and XGBoost could reveal interaction terms without explicit specification. Quite intriguing!
I’m delighted that R users can have access to the incredible Hugging Face pre-trained models. In this demonstration, we provide a straightforward example of how to utilize them for sentiment analysis using GPT-generated synthetic data from evaluation comments. Let’s go!