- Jun 2022
- Feb 2022
- Jan 2022
Une distribution gaussienne pour les notes des étudiants n’est pas un indicateur de rigueur.
Curving grades remains an unquestioned practice, in some contexts. Despite all the debunking which has been done since Terman's (in)famous work...
- Jun 2020
Liu, Andrew, and Mason A. Porter. ‘Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture’. Physical Review E 101, no. 6 (9 June 2020): 062305. https://doi.org/10.1103/PhysRevE.101.062305.
- smaller probabilities
- spatial strength centrality
- spatial embeddings
- longer edges
- geographical fitness
- latent space
- synthetic network
- May 2020
Statistical Modeling, Causal Inference, and Social Science. (2020 April 22). Blog Post: New analysis of excess coronavirus mortality; also a question about poststratification. https://statmodeling.stat.columbia.edu/2020/04/22/analysis-of-excess-coronavirus-mortality-also-a-question-about-poststratification/
- death rate
- synthetic control
- data analysis
- Gaussian Process
- Sep 2018
conditional distribution for individual components can be constructed
So the conditional distribution is conditioned on other components?
calculation once again involves inverting a NxN matrix as in the kernel space representation of regression
this is why we use MCMC or other distribution sampling technique instead
in equation B for the marginal of a gaussian, only the covariance of the block of the matrix involving the unmarginalized dimensions matters! Thus “if you ask only for the properties of the function (you are fitting to the data) at a finite number of points, then inference in the Gaussian process will give you the same answer if you ignore the infinitely many other points, as if you would have taken them all into account!”(Rasmunnsen)
key insight into Gaussian processes