DeepAI AI Chat
Log In Sign Up

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

by   Takafumi Kajihara, et al.

We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihoods. The proposed method is recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why this approach works, showing (for the population setting) that the point estimate obtained with this method converges to the true parameter as recursion proceeds, under a certain assumption. We conduct a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that our method outperforms existing approaches in most cases.


Intractable Likelihood Regression for Covariate Shift by Kernel Mean Embedding

Simulation plays an essential role in comprehending a target system in m...

Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification

This paper explores learning emulators for parameter estimation with unc...

Model Selection for Simulator-based Statistical Models: A Kernel Approach

We propose a novel approach to model selection for simulator-based stati...

Statistical applications of contrastive learning

The likelihood function plays a crucial role in statistical inference an...

Prepaid parameter estimation without likelihoods

In various fields, statistical models of interest are analytically intra...

Capturing positive utilities during the estimation of recursive logit models: A prism-based approach

Although the recursive logit (RL) model has been recently popular and ha...

Solving Non-identifiable Latent Feature Models

Latent feature models (LFM)s are widely employed for extracting latent s...