kimukasetsu

As others have pointed out, it is a good idea to encode domain knowledge in your time series model through specification and priors. Prophet rarely beats a well specified GLM or SARIMA in real world applications, especially when uncertainty estimates are needed. Professionally, I have succesfully applied Gaussian Processes to many such cases.

A GP is an intuitive and expressive way to code time covariance in a model. A famous example is the relative birthdays model, discussed by Gelman et al in Bayesian Data Analysis and here [1].

[1] https://avehtari.github.io/casestudies/Birthdays/birthdays.h...

The biggest mistake engineers make is determining sample sizes. It is not trivial to determine the sample size for a trial without prior knowledge of effect sizes. Instead, I would recommend using a sequential testing framework: set a stopping condition and perform a test for each new batch of sample units instead of waiting for a fixed sample size.

This is called optional stopping and it is not possible using a classic t-test, since Type I and II errors are only valid at a determined sample size. However, other tests make it possible: see safe anytime-valid satistics [1, 2] or, simply, bayesian testing [3, 4].

[1] https://arxiv.org/abs/2210.01948

[2] https://arxiv.org/abs/2011.03567

[3] https://pubmed.ncbi.nlm.nih.gov/24659049/

[4] http://doingbayesiandataanalysis.blogspot.com/2013/11/option...

I had a similar experience. I took psylocibin a few times, but after the last one I started getting panic attacks and feeling derealization quite often. This led to negative recurring thoughts and I felt really bad for a while. Got better after a year of cognitive behavioral therapy and six months taking a light dosage of antidepressants. Routine exercises and zen meditation helped tremendously as well. Get help, it is possible. Your brain can heal itself with your help.

I have a similar setup with Emacs + Evil + Poly + Quarto Modes. Evil for vim keybindings, Poly to read cells as buffers and Quarto as a superior alternative to Jupytext [1].

[1] https://quarto.org/

This.

Multilevel models relax the assumption of independent observations by specifying that the measures of repeated experimental units are dependent on each other. It's a way of telling your model that it has less information than it would have if all observations came from independent units. Therefore, standard errors of effects are usually larger. Otherwise, they are biased [1].

Since most researches are not aware of multilevel models, they design their experiments and aggregate their data to fit the independence assumption, which is rarely a good idea. Many are not even aware of modeling beyond hypothesis tests, and are unable or unwilling to adjust their analysis for confounding factors or non-sampling errors that arise due to experiment design flaws.

Also, p-values should be deprecated, since a) nil hypothesis are strawmen at best and false by definition at worst [2] and b) they incentivize researchers to not think hard about effect sizes and uncertainty in their problems.

[1] https://academic.oup.com/biomet/article/73/1/13/246001 [2] http://www.stat.columbia.edu/~gelman/research/published/fail...

This. Copilot enables bad programming practices imo, especially through clunky APIs like pandas.

RMarkdown has neither of these issues, and it supports Python. It is baffling to me that most data scientists use Jupyter, since its diffs are meaningless. Its export options are very underwhelming compared to Rmd as well. Notebooks [1] are simply a special case of R Markdown formats. Besides, Rmd are literally text files that work with any text editor, including vim.

[1] https://bookdown.org/yihui/rmarkdown/notebook.html

I strongly agree with this. Not to mention parameter interpretability and, in the case of Bayesian models, uncertainty estimates and convergence diagnostics. Such things are very important when making decision under uncertainty. Kaggle competitions and empirical benchmarks are very biased samples of model performance in real life.

I feel these two things often influence too much the course of Machine Learning research and communities, and this is not good. Most ML researchers and pratictioners are barely aware of the latest advances in parametric modelling, which is a shame. Multilevel models allow you to model response variables with explicit dependent structures. This is done through random (sometimes hierarchical) effects constrained by variance parameters. These parameters regularize the effects themselves and converge really well when fitting factors with high cardinality.

Also, multilevel models are very interesting when it comes to the bias-variance tradeoff. Having more levels in a distribution of random effects actually DECREASES [1] overfitting, which is fascinating.

[1] https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixe...