Domino OR-gate (Wikipedia) |
- Looper repo provides a resource list for causal inference looper
- Thanks to Patrick McCrae for invoking ontology inference comparison.
Domino OR-gate (Wikipedia) |
Problem-solving is the core activity of data science using scientific principles and evidence. On our side, there is an irresistible urge to solve the most generic form of the problem. We do this almost always from programming to formulation of the problem. But, don't try to solve a generalised version of the problem. Solve it for N=1 if N is 1 in your setting, not for any integer: Save time and resources and try to embed this culture to your teams and management. Extent later when needed on demand.
This generalisation phenomenon manifests itself as an algorithmic design: From programming to problem formulation, strategy and policy setting. The core idea can be expressed as mapping, let's say the solution to a problem is a function, mapping from one domain to a range
$$ f : \mathbb{R} \to \mathbb{R} $$
Trying to solve for the most generic setting of the problem, namely multivariate setting
$$ f : \mathbb{R}^{m} \to \mathbb{R}^{n} $$
where $m, n$ are the integers generalising the problem.
It is elegant to solve a generic version of a problem. But is it really needed? Does it reflect reality and would be used? If N=1 is sufficient, then try to implement that solution first before generalising the problem. An exception to this basic pattern would be if you don't have a solution at N=1 but once you move larger N that there is a solution: you might think this is absurd, but SVM works exactly in this setting by solving classification problem for disconnected regions.
Postscripts
Figure 1: VGG architecture spectral difference in the long positive tail [suezen20a]. |
Catalan Castellers are collaborating (Wikipedia) |
Artificial Intelligence Engines: An introduction to the Mathematics of Deep Learning by Dr James V. Stone the book and Github repository. (c) 2019 Sebtel Press |
Please refrain on posting a blog or similar posts on infection modelling and giving advice out of your ad-hoc data analysis you did over your lunch-break, if you have not worked on computational epidemiology before. There is a vast academic literature on computational epidemiology. Let people experts in those fields express their modelling efforts first. Let us value expertise in an area.
PCC in Python (Author). |