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.



  • 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)


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


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



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


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


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

neural networks


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


  • Tensorflow



  • 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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