Amortised Inference through One-Step Implicit Sampling
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms.
ProofPaper ↗
Key points
- In this work, we build upon recent progress in data-driven generative modelling to offer a possible solution for one-step amortised sampling.
- Our one-step implicit sampler (OSIS) uses short-time MCMC dynamics to define a local target for the evolution of an implicitly modelled distribution.
- We analyse the convergence properties of OSIS and show connections with other generative modelling and sampling algorithms.
- We study various design choices and show that OSIS performs well compared to multi-step amortised samplers and non-amortised MCMC baselines while being far more efficient at sampling time.
Sources (1)
- [1]Amortised Inference through One-Step Implicit SamplingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:01 PM
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms.
In this work, we build upon recent progress in data-driven generative modelling to offer a possible solution for one-step amortised sampling.
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