flusteredBias

I use it and like it.

It’s 2025 and I bought 3 typewriters just this year. I’m fired.

This is anecdotal and I hope someone who has some research experience can say whether this is true or not generally, but I recently got a Kindle and found that if I use really large font sizes where there are fewer than 50 words on a page it's easier for me to stay engaged. Maybe this has something to do with cognitive load or chunking information. Some fonts look quite a bit better at these large sizes. So for me I don't think typography alone is sufficient. I think the interaction between a large font size and a typography that looks pleasing at a large font size helps with engagement.

... so dplyr.

How does this compare to bearblog

ECDF plots are what I use.

I do the same with DuckDB and pretty print with tidy-viewer.

The only way I do that is with git.

Given that I have no clue what a multi-user story is I am guessing not.

I kind of think quarto is a much better solution to the problems that notebooks try to solve plus you get the added bonus of having plain text as the file source.

How the parameters are estimated it not the message.

In statistics there are latin letters and greek letters. When you see a symbol denoted as a greek letter then that is a population parameter. When you see a latin letter that is a sample estimate. It could be Frequentist, Bayesian, Likelihoodist, Fiducial, Empirical Bayes, etc. Theoretical population greeks or sample calculated latins.

I apologize. ^This comment was to harsh.

Statistics can be summarizes as one thing n -> N. Does ‘little n’ represent ‘big N’. In other words, does the sample generalize to the population. Statistics means something like “description of the state”. It was born out of census samples where larger population samples had to be estimated. “n” could be a handful of fish in a “N” lake. “n” could also be the parameter estimated in a linear regression with the sample of data collected while “N” is the true parameter of the relationship if we had all the data. Point estimation is about finding the needle in the haystack, but much more often statistics is about finding the haystack given the needle. One tool statistics uses to get to the haystack is probability.

I am going to defined the readme a little

- they often don’t support missing/corrupt data

Clean data would benefit most models, not just non-deep learning models. Missing data introduces bias even in DL models.

- they focus on linear relationships and not complex joint distributions

1. If seasonality is present. Which is usually the case in practical business problems then you will find that actual ~ lag_actuals explains most of the variance with a linear relationship. non-linearities in time series is not something that I see often. You can usually make a feature that explains those away linearly.

- they focus on fixed temporal dependence that must be diagnosed and specified a priori

Not sure sure you are saying here.

- they take as input univariate, not multiple interval, data

That is not the case. Time series regression can take many lags for inputs.

- they focus on one-step forecasts, not long time horizons

False. Time series regression models are used to forecast revenue many years into the future in the business world.

- they’re highly parameterized and rigid to assumptions

So is F=ma

- they fail for cold start problems

Because a cold start is not a time series data set. Why would time series methods work on non time series data.