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 ...

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? ...

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! picture resembles interaction 🤣 Objectives: What is interaction? Simulate interaction Visualize interaction True Model ✅ Wrong Model ❌ What is S Learner? What is ...

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! Interesting Problem 😎 What if you’re faced with ...

The PyWhy Causal-learn Discord community is fantastic! The package documentation is equally impressive, making experiential learning both fun and informative. Truly, it’s another exceptional tool for causal discovery at our fingertips! ❤️ It’s time to delve into PyWhy’s Causal-learn! his brief blog post leverages the framework from a ...

Get ready for a thrill ride in causal discovery! We’re diving into gCastle, a Python package, right in R to amp up our skills. Let’s orchestrate our prior knowledge and nail that true DAG. 🔥 As I delve into Aleksander Molak’s Causa...

Simulating a binary dataset, coupled with an understanding of the logit link and the linear formula, is truly fascinating! However, we must exercise caution regarding our adjustments, as they can potentially divert us from the true findings. I advocate for transparency in Directed Acyclic Graphs (DAGs) and emphasize the sequence: ...

Saving can be enjoyable! If you’re planning to cut down on takeout orders, why not use past data to simulate your savings? Let it inspire and motivate your future dining-in decisions! 👍 If you’re anything like me, you might often resort ...

Beware of what we adjust. As we have demonstrated, adjusting for a collider variable can lead to a false estimate in your analysis. If a collider is included in your model, relying solely on AIC/BIC for model selection may provide misleading results and give you a false sense of ...

Front-door adjustment: a superhero method for handling unobserved confounding by using mediators (if present) to estimate causal effects accurately. Let’s DAG an estimand! library(dagitty) dag

Special thanks are due to Dr. Amit Diwakar, Dr. Jeffrey Solomon, and Jennifer Hayes for their contributions in making this project a resounding success and continuously striving for improvement. I had the pleasure of presenting our project at Shi...

Biweekly did great overall, Better at TIPS, short-term bond, real estate, international; Monthly is second best with 3 out of 8. Good at small, mid, large caps If you want to automate your investment on index funds with a fixed amount every mont...

TL;DR Bring a textbook to life by Using a simple Natural Language Processing method (Ngram) to guide focused reading and build a robust differential diagnosis Introduction What would you do if you encountered a clinical case with quite a few nuance symptoms? How do you know which article to ...

Inspiration This was inspired by wanting to read a textbook Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases. But, I didn’t want to read the textbook in sequence. Hence, I figured that if I read a par...

Just Because == Randomness Randomness can be difficult to simulate because we are biased. Not that thinking of buying your spouse 😍 flowers is a bad thing. What if you can preserve that idea and actualization by coding that! But how? Thoug...

Brief Introduction: The 100 prisoners problem is a probability theory and combinatorics problem. In this challenge, 100 numbered prisoners must find their own numbers in one of 100 drawers in order to survive. Rules: ...