Models, Algorithms and Software for Statistical Inference

I’m often asked by recruiters, clients, academics or colleagues which models I’ve worked with. This post is an effort to put this all in one place. All of these models, algorithms, and software I’ve worked with in some capacity. This includes implementation, development, application, required understanding, or any combination thereof. This list in non-exhaustive. I’ll try to update this list as I remember. My experience is mostly Bayesian, and these modeling techniques are often used in combination with one another.

Bayesian

models

  • hierarchical/multilevel models
  • Gaussian process models
  • non-linear regression models
  • linear regression models
  • survival models
  • mixture models
  • regularized regression models
  • graph regression models
  • auto-regressive models or random walk models
  • 1-D time series models (GP or AR models)
  • dose response models
  • functional models (GPs)

algorithms

  • Gibbs samplers (scratch, translation of complex models)
  • parameter expanded Gibbs samplers
  • Metropolis-Hastings
  • Metropolis Adjusted Langevin algorithm
  • Hamiltonian Monte Carlo
  • Slice samplers

software

  • Stan
  • samplers in R
  • samplers in C++

optimization

models

  • classical linear or general linear models
  • SISO/MIMO radar models
  • convex optimization models
  • filters for radar using optimization

software

  • R: lm, glmer, glmnet
  • CVX
  • CVXpy

algorithms

  • gradient ascent/descent
  • Newton’s method
  • Laplace Approximation
  • the EM algorithm

neural networks

models

  • multi-layer perceptrons (MLP)
  • convolutional neural networks (CNN)

software

  • Tensorflow

miscellaneous

autodifferentation

  • Stan math library’s autodiff

miscellaneous numerical linear algebra

  • QR Factorization
  • LU Decomposition
  • Cholesky Factorization
  • SVD
  • Householder transformation
  • Schur complement

programming languages

  • Linux
  • R
  • C++
  • python
  • JAVA
  • Stan
  • Tensorflow

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