peer reviewedMany domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science
The standard approach to inference from cosmic large-scale structure data employs summary statistics...
Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing...
The last century has seen a growing interest in complexity in economics and social sciences. The nee...
Many domains of science have developed complex simulations to describe phenomena of interest. While ...
Science makes extensive use of simulations to model the world. Statistical inference identifies whic...
This dissertation presents several novel techniques and guidelines to advance the field of simulatio...
Simulators often provide the best description of real-world phenomena; however, they also lead to ch...
We summarize and discuss new inference techniques for systems that are described by a simulator wit...
peer reviewedWe present extensive empirical evidence showing that current Bayesian simulation-based ...
We present extensive empirical evidence showing that current Bayesian simulation-based inference alg...
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning techn...
Statistics Views asked Dr Tintle to explain more about simulation-based inference in statistics educ...
Complex statistical models pose a great challenge to practitioners because of methodological and com...
According to the most common interpretation of the simulation argument, we are very likely to live i...
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoSta...
The standard approach to inference from cosmic large-scale structure data employs summary statistics...
Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing...
The last century has seen a growing interest in complexity in economics and social sciences. The nee...
Many domains of science have developed complex simulations to describe phenomena of interest. While ...
Science makes extensive use of simulations to model the world. Statistical inference identifies whic...
This dissertation presents several novel techniques and guidelines to advance the field of simulatio...
Simulators often provide the best description of real-world phenomena; however, they also lead to ch...
We summarize and discuss new inference techniques for systems that are described by a simulator wit...
peer reviewedWe present extensive empirical evidence showing that current Bayesian simulation-based ...
We present extensive empirical evidence showing that current Bayesian simulation-based inference alg...
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning techn...
Statistics Views asked Dr Tintle to explain more about simulation-based inference in statistics educ...
Complex statistical models pose a great challenge to practitioners because of methodological and com...
According to the most common interpretation of the simulation argument, we are very likely to live i...
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoSta...
The standard approach to inference from cosmic large-scale structure data employs summary statistics...
Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing...
The last century has seen a growing interest in complexity in economics and social sciences. The nee...