This section simulates pair programming with ChatGPT, where the human acts as the navigator and the AI writes code for statistical modeling. Starting with simple tasks, the complexity increases, exposing both ChatGPT's coding strengths and reasoning limitations. The team evaluates its performance on tasks such as density evaluation, ML estimation, sampling, visualization, and parallelization using the Clayton copula. Despite inconsistencies in reasoning, ChatGPT proves useful as a coding assistant, providing effective solutions in custom software development and tailored applications.
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