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Efficient Probabilistic Inference in the Quest for Physics Beyond the
  Standard Model

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

20 July 2018
A. G. Baydin
Lukas Heinrich
W. Bhimji
Lei Shao
Saeid Naderiparizi
Andreas Munk
Jialin Liu
Bradley Gram-Hansen
Gilles Louppe
Lawrence Meadows
Philip Torr
Victor W. Lee
P. Prabhat
Kyle Cranmer
Frank D. Wood
ArXivPDFHTML

Papers citing "Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model"

19 / 19 papers shown
Title
Flow Matching for Posterior Inference with Simulator Feedback
Flow Matching for Posterior Inference with Simulator Feedback
Benjamin Holzschuh
Nils Thuerey
35
0
0
29 Oct 2024
Efficient Data Mosaicing with Simulation-based Inference
Efficient Data Mosaicing with Simulation-based Inference
Andrew Gambardella
Youngjun Choi
Doyo Choi
Jinjoon Lee
23
0
0
26 Oct 2022
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Andreas Munk
A. Mead
Frank D. Wood
17
2
0
21 Oct 2022
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software
Mario Beraha
Daniel Falco
A. Guglielmi
13
8
0
20 Jul 2021
Meta-Learning an Inference Algorithm for Probabilistic Programs
Meta-Learning an Inference Algorithm for Probabilistic Programs
Gwonsoo Che
Hongseok Yang
TPM
13
1
0
01 Mar 2021
Variational Inference for Deblending Crowded Starfields
Variational Inference for Deblending Crowded Starfields
Runjing Liu
Jon D. McAuliffe
Jeffrey Regier
BDL
16
10
0
04 Feb 2021
Gaussian Process Bandit Optimization of the Thermodynamic Variational
  Objective
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu-Linh Nguyen
Vaden Masrani
Rob Brekelmans
Michael A. Osborne
Frank D. Wood
11
5
0
29 Oct 2020
Simulation-based inference methods for particle physics
Simulation-based inference methods for particle physics
Johann Brehmer
Kyle Cranmer
AI4CE
30
21
0
13 Oct 2020
Simulation-Based Inference for Global Health Decisions
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt
Bradley Gram-Hansen
Nantas Nardelli
Andrew Gambardella
R. Zinkov
...
N. Siddharth
A. B. Espinosa-González
A. Darzi
Philip H. S. Torr
A. G. Baydin
AI4CE
23
4
0
14 May 2020
Planning as Inference in Epidemiological Models
Planning as Inference in Epidemiological Models
Frank D. Wood
Andrew Warrington
Saeid Naderiparizi
Christian Weilbach
Vaden Masrani
...
Adam Scibior
Boyan Beronov
John Grefenstette
Duncan Campbell
Alireza Nasseri
17
6
0
30 Mar 2020
Adaptive Divergence for Rapid Adversarial Optimization
Adaptive Divergence for Rapid Adversarial Optimization
M. Borisyak
T. Gaintseva
Andrey Ustyuzhanin
11
0
0
01 Dec 2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic
  Programs with Stochastic Support
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuanshuo Zhou
Hongseok Yang
Yee Whye Teh
Tom Rainforth
TPM
29
19
0
29 Oct 2019
Attention for Inference Compilation
Attention for Inference Compilation
William Harvey
Andreas Munk
A. G. Baydin
Alexander Bergholm
Frank D. Wood
17
9
0
25 Oct 2019
Amortized Rejection Sampling in Universal Probabilistic Programming
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi
Adam Scibior
Andreas Munk
Mehrdad Ghadiri
A. G. Baydin
...
R. Zinkov
Philip H. S. Torr
Tom Rainforth
Yee Whye Teh
Frank D. Wood
16
7
0
20 Oct 2019
Distilling Importance Sampling for Likelihood Free Inference
Distilling Importance Sampling for Likelihood Free Inference
D. Prangle
Cecilia Viscardi
11
3
0
08 Oct 2019
Hijacking Malaria Simulators with Probabilistic Programming
Hijacking Malaria Simulators with Probabilistic Programming
Bradley Gram-Hansen
Christian Schroeder de Witt
Tom Rainforth
Philip H. S. Torr
Yee Whye Teh
A. G. Baydin
23
8
0
29 May 2019
An Introduction to Probabilistic Programming
An Introduction to Probabilistic Programming
Jan Willem van de Meent
Brooks Paige
Hongseok Yang
Frank D. Wood
GP
15
196
0
27 Sep 2018
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment
  using Deep Neural Networks
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Evan Racah
Seyoon Ko
Peter Sadowski
W. Bhimji
C. Tull
Sang-Yun Oh
Pierre Baldi
P. Prabhat
32
32
0
28 Jan 2016
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,262
0
09 Jun 2012
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