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Name: Samuel Wiqvist

Type: User

Bio: PhD in mathematical statistics from Lund University, Sweden, now data scientist at Danske Bank in the Fraud Machine Learning, Data & Analytics team

Location: Sweden

Blog: https://samuelwiqvist.github.io/

Some of my work

Research

Sequential Neural Posterior and Likelihood Approximation

A paper on simulation-based inference where we present an algorithm that learns both the posterior and the likelihood of the model.
Efficient inference for stochastic differential mixed-effects models using correlated particles pseudo-marginal algorithms

In this paper, we use correlated particle pseudo-marginal Metropolis-Hastings (CPMMH) for accelerating inference for stochastic differential equation mixed-effects models (SDEMEMs).
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

A deep learning paper where we introduce the novel architecture partially exchangeable networks (PEN). In the paper, we use PEN for learning summary statistics for ABC.
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

An MCMC paper where we develop an accelerated version of the delayed-acceptance algorithm. As a case study, we consider the computational challenging and highly complex problem of modeling protein folding.

Tutorials

Implementation of some ABC algorithms in Julia

Generic implementations of some ABC algorithms in Julia.
Introduction to likelihood-free inference

An introductory Jupyter notebook tutorial on likelihood-free inference.

Talks

Pioneers-pp: Bayesian statistics and ABC talk

Slides, examples, and computer exercises for my presentation at the Pioneers of Probabilistic Programming Meet-up group.
Bayes@Lund 2019

Slides for my talk at Bayes@Lund 2019.

Other

Useful terminal/git commands

A list of some useful terminal/git commands that I regularly use.

Samuel Wiqvist's Projects

abcmcmcrickermodel

ABC-MCMC implementation for the Ricker model

adamcmcpaper

Algorithms and case studies for the paper "Accelerating delayed-acceptance Markov chain Monte Carlo algorithms".

algorithms_and_data_structures

Home work for the course "ALGORITHMS and DATA STRUCTURES"

approximatebayesiancomputation.jl

Julia implementation of some ABC algorithms.

bayesatlund2019presentation

Presentation for Bayes@Lund 2019

binder-demo-julia1.0.0

coderefinery-hackathon-2019-project-2

Coderefinery hackathon project

compute_mcmd

Code for the course Monte Carlo and Molecular Dynamics Tools 2019

distributions.jl

A Julia package for probability distributions and associated functions.

dl-compute-project

Project work for the Compute DL course

efficient_sdemem

Code for the paper: "Efficient inference for stochastic differential mixed-effects models using correlated particles pseudo-marginal algorithms"

exjobb

Code for my master thesis

fmnn25project1

First project for FMNN25 Advanced Numerical Algorithms

gaussianprocesses.jl

A Julia package for Gaussian Processes

hamiltonianabc.jl

Hamiltonian Approximate Bayesian Computation for Julia.

inference_course_em_alg

Home work for the course "Statistical Inference"

introlikelihoodfree

Some simple examples on likelihood-free methods.

julia-python

Installing Julia and Python environments

mxnet.jl

MXNet Julia Package - flexible and efficient deep learning in Julia

pens-and-abc

Code for the paper "Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation"

pioneers-pp-abc-talk

Pioneers-pp presentation, and code examples

project_02901_adv_ml

Project work for 02901 Advanced Topics in Machine Learning: Computational Tools for Machine Learning in Python

ram_example

samuelwiqvist

samuelwiqvist.github.io

Personal homepage

sim-gen-gauss-markov-model

Simulations for the generlized Gauss-Markov model

snpla

Code for the paper "Sequential Neural Posterior and Likelihood Approximation"

stats_inference_course_bayesian_bits

Home work for the course "Statistial Inference"

stats_inference_hw1

Home work for the course "Statistial Inference"

statsbase.jl

Basic statistics for Julia

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