Tobias Kohn

2018

A researcher in the Science corpus: 1 paper; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Papers from Tobias Kohn's perspective

Ordered by how strongly the researchers Tobias Kohn trusts endorse each paper. Same corpus, a different order for every researcher.

1. A New Approach to Probabilistic Programming Inference
2. Probabilistic programming in Python using PyMC3
3. Hybrid Monte Carlo
4. Interacting Particle Markov Chain Monte Carlo
5. PyMC: Bayesian Stochastic Modelling in Python (Journal of Statistical Software)
6. Church: a language for generative models
7. Venture: a higher-order probabilistic programming platform with programmable inference
8. Monte Carlo Statistical Methods
9. Sequential Monte Carlo Methods in Practice
10. BUGS - Bayesian inference Using Gibbs Sampling Version 0.50

Their papers

• Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs (2018) — 3 citations