Here, we elaborate a model of visual foraging—in a hierarchical context—wherein agents infer a higher-order visual pattern (a “scene”) by sequentially sampling ambiguous cues. Inspired by previous ...
Conversely, persistent surprise is the hallmark of a bad model. Figure 3. A graphical summary of computational and physiological key components of hierarchical Bayesian inference. Computationally, ...
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Abstract: Due to the limited energy and computing resources of Internet of Things (IoT) devices ... We propose a hierarchical approach to decompose the problem, and then solve it with a proportional ...
Bayesian reasoning is a statistical method that helps individuals make inferences and decisions based on probabilities. It is particularly useful in situations where uncertainty is present ...
By employing a hierarchical Bayesian inference model, the study demonstrated that humans can simultaneously process statistical regularities and deterministic rules when learning sequences.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...
Combining various data sources and other types of information is becoming increasingly important in various types of analyses. Certain classes of Bayesian hierarchical models have shown to be ...
Combining various data sources and other types of information is becoming increasingly important in various types of analyses. Certain classes of Bayesian hierarchical models have shown to be ...
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While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for ...
The seven decades of "artificial intelligence" have been marked by exaggerated promises, surprising developments and ...
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