Science and math
In this collection of articles I’m trying to answer some of the questions that arise in doing science, math and programming, with a focus on network science.
-
The Woodbury matrix identity
-
Eigenvectors from eigenvalues a numpy implementation
-
The link between machine learning and statistical physics
-
Quantum statistical mechanics of complex networks in networkqit
-
Hidden Markov Models
-
Low rank estimation of sparse observed ratios
-
Smooth approximation to the floor function
-
Einstein summation in Numpy
-
Showing brain parcellation in Python with nilearn and some hacking
-
Weighted graphs from adjacency matrix in graph-tool
-
Random matrix ensemble spectral density and the average resolvent
-
Some considerations about the spectral entropies framework
-
Dataset of biologically derived graphs
-
Why probabilistic programming matters
-
Inequalities, traces, densities, matrices
-
An identity for zero
-
Distance matrix of the air500 complex network
-
Lambda functions composition in Python 3
-
A Graduate Course in Econometrics
-
A Full Undergraduate Course in Econometrics
-
The Enhanced weighted random graph model (EWRG)
-
Plugging the weighted random graph into Surprise
-
Eigenvalue spectra of modular matrix techniques and calculations
-
Computing Euler angles from 3x3 rotation matrix in Matlab
-
Computing hypergeometric probability efficiently in C++
-
Comparing spectral densities of random graph models.
-
Detecting the consensus communities in graphs