In which fields graphical models are better then neural networks?
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Graphical models are better than neural networks in the field of representing joint probability distributions with an underlying conditional dependence structure . They are widely used in science to model relationships between features and capture the dependency structure between them . Graphical models, such as Bayesian and Markov networks, can represent complex dependency functions and provide insights from real-world data . They can fit generic graph structures and support mixed input data types . In contrast, neural networks are better suited for approximating complex function representations and can be used as function approximators in graphical model learning . Neural networks are effective in learning temporal dependencies in time series data and have been proven useful in asset management .
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15 Oct 2020 | The provided paper does not explicitly mention whether graphical models are better than neural networks in any specific fields. |
Open access•Proceedings Article 01 Jun 2020 | The paper does not explicitly mention any fields where graphical models are better than neural networks. |
Open access•Posted Content | The provided paper does not explicitly mention any fields where graphical models are better than neural networks. |
| The provided paper does not explicitly mention any fields in which graphical models are better than neural networks. | |
| The paper does not explicitly mention any fields in which graphical models are better than neural networks. |
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