Compositional simulation-based inference for time series

• URL: http://arxiv.org/abs/2411.02728v1
• Date: Tue, 05 Nov 2024 01:55:07 GMT
• Title: Compositional simulation-based inference for time series
• Authors: Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke,
• Abstract summary: simulators frequently emulate real-world dynamics through thousands of single-state transitions over time. We propose an SBI framework that can exploit such Markovian simulators by locally identifying parameters consistent with individual state transitions. We then compose these local results to obtain a posterior over parameters that align with the entire time series observation.
• Score: 21.9975782468709
• License:
• Abstract: Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this approach avoids the need for tractable likelihoods, it often requires a large number of simulations and has been challenging to scale to time-series data. Scientific simulators frequently emulate real-world dynamics through thousands of single-state transitions over time. We propose an SBI framework that can exploit such Markovian simulators by locally identifying parameters consistent with individual state transitions. We then compose these local results to obtain a posterior over parameters that align with the entire time series observation. We focus on applying this approach to neural posterior score estimation but also show how it can be applied, e.g., to neural likelihood (ratio) estimation. We demonstrate that our approach is more simulation-efficient than directly estimating the global posterior on several synthetic benchmark tasks and simulators used in ecology and epidemiology. Finally, we validate scalability and simulation efficiency of our approach by applying it to a high-dimensional Kolmogorov flow simulator with around one million dimensions in the data domain.

Related papers

• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [65.24988062003096]
  We present NAVSIM, a framework for benchmarking vision-based driving policies. Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other. NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights.
  arXiv  Detail & Related papers  (2024-06-21T17:59:02Z)
• All-in-one simulation-based inference [19.41881319338419]
  We present a new amortized inference method -- the Simformer -- which overcomes current limitations. The Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks. It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data.
  arXiv  Detail & Related papers  (2024-04-15T10:12:33Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Neural Likelihood Approximation for Integer Valued Time Series Data [0.0]
  We construct a neural likelihood approximation that can be trained using unconditional simulation of the underlying model. We demonstrate our method by performing inference on a number of ecological and epidemiological models.
  arXiv  Detail & Related papers  (2023-10-19T07:51:39Z)
• Simulation-based inference using surjective sequential neural likelihood estimation [50.24983453990065]
  Surjective Sequential Neural Likelihood estimation is a novel method for simulation-based inference. By embedding the data in a low-dimensional space, SSNL solves several issues previous likelihood-based methods had when applied to high-dimensional data sets.
  arXiv  Detail & Related papers  (2023-08-02T10:02:38Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• Truncated Marginal Neural Ratio Estimation [5.438798591410838]
  We present a neural simulator-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior. By estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results.
  arXiv  Detail & Related papers  (2021-07-02T18:00:03Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time [5.533353383316288]
  We present algorithms for nested neural likelihood-to-evidence ratio estimation and simulation reuse. Together, these algorithms enable automatic and extremely simulator efficient estimation of marginal and joint posteriors.
  arXiv  Detail & Related papers  (2020-11-27T19:00:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.