Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912462497906688 |
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| author | Bae, Stefano Bocchi, Dario Del Bono, Luca Maria Leuzzi, Luca |
| author_facet | Bae, Stefano Bocchi, Dario Del Bono, Luca Maria Leuzzi, Luca |
| contents | Inverse problems arise in situations where data is available, but the underlying model is not. It can therefore be necessary to infer the parameters of the latter starting from the former. Statistical mechanics offers a toolbox of techniques to address this challenge. In this work, we illustrate three of the main methods: the Maximum Likelihood, Maximum Pseudo-Likelihood, and Mean-Field approaches. We begin with a thorough theoretical introduction to these methods, followed by their application to inference in several well-known statistical physics systems undergoing phase transitions. Namely, we consider the ordered and disordered Ising models, the vector Potts model, and the Blume-Capel model on both regular lattices and random graphs. This discussion is accompanied by a GitHub repository that allows users to both reproduce the results and experiment with new systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Bae, Stefano Bocchi, Dario Del Bono, Luca Maria Leuzzi, Luca Statistical Mechanics Disordered Systems and Neural Networks Data Analysis, Statistics and Probability Inverse problems arise in situations where data is available, but the underlying model is not. It can therefore be necessary to infer the parameters of the latter starting from the former. Statistical mechanics offers a toolbox of techniques to address this challenge. In this work, we illustrate three of the main methods: the Maximum Likelihood, Maximum Pseudo-Likelihood, and Mean-Field approaches. We begin with a thorough theoretical introduction to these methods, followed by their application to inference in several well-known statistical physics systems undergoing phase transitions. Namely, we consider the ordered and disordered Ising models, the vector Potts model, and the Blume-Capel model on both regular lattices and random graphs. This discussion is accompanied by a GitHub repository that allows users to both reproduce the results and experiment with new systems. |
| title | Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition |
| topic | Statistical Mechanics Disordered Systems and Neural Networks Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2507.02574 |