SOURCE-LINKED INTELLIGENCE
Neural composite likelihood estimation: simulation based inference for time series
Simulation based inference (SBI) circumvents the challenge of intractable likelihoods by using a simulator that generates data given parameter values. For instance, neural likelihood estimation (NLE) estimates the likelihood function by training a neural network to perform conditional density estimation on simulated data given corresponding parameters. However such density estimation is only feasible for relatively low dimensional data. We extend the scalability of SBI methods to a higher dimensional problem: long sequences with a complex dependency structure. We introduce Neural Composite Lik
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T13:31:50.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.