Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to evaluate. Extensions of the basic ABC rejection algorithm have improved the computational efficiency of the procedure and ...
An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statistics and Computing, 22(5):1009-1020, 2012.P. Del Moral, A. Doucet, and A. Jasra. An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statistics and Computing, 22(5):1009-1020, 2012...
Approximate computing Autonomous driving Edge computing Positioning Smart sensing 1. Introduction Computing contributes significantly to the world’s rising energy consumption. In 2018, data centers in the EU accounted for 76.8TWh or 2.7% of the total electricity demand, and this number is predicted ...
Approximate Bayesian Computation (ABC) is a data-driven approach, which uses experimental or higher fidelity data to approximate the probability distribution of model parameters. ABC is based on the Bayesian approach but does not require knowing the analytical expression for a likelihood function. The...
Fig. 5: Inference of independent domestication events of apricot, with divergence in the face of gene flow, using random-forest approximate Bayesian computation combined with coalescent-based simulations. afastSTRUCTURE barplot for the pruned dataset used for random-forest approximate Bayesian computation...
approximate the (generally) expensive objective function. This surrogate, in turn, undergoes Bayesian updates as new information about the design space is acquired, according to a predefined acquisition policy. The use of a Bayesian surrogate model does not impose any a priori restrictions (such as...
Bayesian learning problems by using a Sequential Monte Carlo (SMC) approach. Indeed, Monte Carlo methods seems to be a natural solution, due to their capability of reaching convergence independently from the integration space dimension. The SMC method approximates the infinite dimensional supportϕ...
In other words, there is the potential for an explicit connection between the normative (approximate Bayesian inference) level and the processes underlying neurobiological implementation. 6. Conclusions In this article, we have cast “homoeostasis” and “behavioural control” in terms of Active ...
The challenge for a downstream circuit is to find a way to approximate these weights, when provided only with incoming spikes, the task feedback, and potentially the modulator, but without explicit knowledge of the stimulus encoding model. How can the brain achieve this? The conventional means ...
Neural operators, i.e., a ML-based surrogate that approximates the integral solution operator of a family of partial differential equations (PDEs) to bypass conventional numerical integration47. Coarse-graining Constructing a surrogate for high-fidelity quantum-state-specific chemistry models to describe...