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Moreover, the neural circuit we design can be easily generalized to implement inference for other problems, such as the multi-stimuli cause inference and the same-different judgment.
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Although deep learning does not make the most popular topic in machine learning until 2006, the research with the similar idea can be traced back to 1990s, for example, Jang presented an adaptive-network-based fuzzy inference system (ANFIS) to implement fuzzy inference with a 5-layered neural network [68], as depicted in Fig. 5.
Teh et al. (2008) have described a way of implementing collapsed variational inference for HDPs.
Finally, we implement the inference procedures to obtain a multi-semantics engine for probabilistic argumentation and demonstrate its usage.
Here we give a short overview of the implemented Bayesian inference strategy, for a more theoretical introduction in ODE modeling and Bayesian inference we refer to Section Background in the supplementary text see Additional file 1.
This has propelled the development of different approaches to implement fuzzy inference systems.
To implement this inference, we used regular expression patterns to detect groups of enumerated entities.
In spite of some attempts to reduce the computational cost [ 27], the Bayesian network approach in general is computationally intensive to implement, especially for network inference on a genome-wide scale.
The main idea is to leverage a state-of-the-art generalized Satisfiability Modulo Theory solver for implementing the inference and separation oracles of Structured Output SVMs.
Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework.
The clique-tree technique and the variable-elimination technique were implemented for efficient inference and learning [11], [20], [25].
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