Jan 31, 2020 · This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses.
This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses.
In this paper we explore the possibility of automating the process of constructing summary statistics by training deep neural networks to predict the parameters ...
Missing: Convolutional | Show results with:Convolutional
Abstract: Approximate Bayesian Computation (ABC) methods are used proximate posterior distributions in models with unknown or computation.
Missing: Convolutional | Show results with:Convolutional
In this paper, we propose the first, to our knowledge, ABC method completely free of summary statistics, distance, and tolerance threshold.
Jan 4, 2023 · In this work, we combine a Gaussian process accelerated ABC method with the automatic learning of summary statistics via graph neural networks.
In this paper we explore the possibility of automating the process of constructing summary statistics by training deep neural networks to predict the parameters ...
Missing: Convolutional | Show results with:Convolutional
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Abstract—In high dimensional data, it is often very difficult to analytically evaluate the likelihood func- tion, and thus hard to get a Bayesian posterior.
Missing: Convolutional | Show results with:Convolutional
Jan 16, 2019 · Our results support a third introgression in all Asian and Oceanian populations from an archaic population.
Missing: Convolutional | Show results with:Convolutional
A Novel Approach for Choosing Summary Statistics in Approximate ...
www.ncbi.nlm.nih.gov › PMC3522150
Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature.
Missing: Convolutional Neural Networks