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Found 6 papers, 4 papers with code
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
To overcome this, we propose preconditioned NPE (PNPE) and its sequential version (PSNPE), which uses a short run of ABC to effectively eliminate regions of parameter space that produce large discrepancy between simulations and data and allow the posterior emulator to be more accurately trained.
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods.
A practical guide to pseudo-marginal methods for computational inference in systems biology
For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable.
Rapid Bayesian inference for expensive stochastic models
In this work, we present new computational Bayesian techniques that accelerate inference for expensive stochastic models by using computationally inexpensive approximations to inform feasible regions in parameter space, and through learning transforms that adjust the biased approximate inferences to closer represent the correct inferences under the expensive stochastic model.
Computation Cell Behavior Molecular Networks
Vector operations for accelerating expensive Bayesian computations -- a tutorial guide
We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments using standard tools.
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling.
Molecular Networks
