We implement a pseudolikelihood approach with l1 and l2 regularizations as well as the recently introduced pseudolikelihood with decimation procedure to the inverse problem in continuous spin models on arbitrary networks, with arbitrarily disordered couplings. Performances of the approaches are tested against data produced by Monte Carlo numerical simulations and compared also to previously studied fully connected mean-field-based inference techniques. The results clearly show that the best network reconstruction is obtained through the decimation scheme, which also allows us to make the inference down to lower temperature regimes. Possible applications to phasor models for light propagation in random media are proposed and discusse
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
The inverse statistical problem of finding direct interactions in complex networks is difficult. In ...
We consider the problem of inferring a causality structure from multiple binary time series by using...
We implement a pseudolikelihood approach with l1 and l2 regularizations as well as the recently intr...
We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference b...
In this Letter we propose a new method to infer the topology of the interaction network in pairwise ...
We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference ...
Statistical Mechanics has gained a central role in modern Inference and Computer Science. Many optim...
The mean-field (MF) approximation offers a simple, fast way to infer direct interactions between ele...
The inverse problem is studied in multi-body systems with nonlinear dynamics representing, e.g., pha...
International audienceThe mean-field (MF) approximation offers a simple, fast way to infer direct in...
Inverse problems in statistical physics are motivated by the challenges of ‘big data’ in different f...
Our research group will consider the study of the statistical mechanics properties, especially at eq...
The Ising model is a celebrated example of a Markov random field, which was introduced in statistica...
Data is scaling exponentially in fields ranging from genomics to neuroscience to economics. A centra...
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
The inverse statistical problem of finding direct interactions in complex networks is difficult. In ...
We consider the problem of inferring a causality structure from multiple binary time series by using...
We implement a pseudolikelihood approach with l1 and l2 regularizations as well as the recently intr...
We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference b...
In this Letter we propose a new method to infer the topology of the interaction network in pairwise ...
We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference ...
Statistical Mechanics has gained a central role in modern Inference and Computer Science. Many optim...
The mean-field (MF) approximation offers a simple, fast way to infer direct interactions between ele...
The inverse problem is studied in multi-body systems with nonlinear dynamics representing, e.g., pha...
International audienceThe mean-field (MF) approximation offers a simple, fast way to infer direct in...
Inverse problems in statistical physics are motivated by the challenges of ‘big data’ in different f...
Our research group will consider the study of the statistical mechanics properties, especially at eq...
The Ising model is a celebrated example of a Markov random field, which was introduced in statistica...
Data is scaling exponentially in fields ranging from genomics to neuroscience to economics. A centra...
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
The inverse statistical problem of finding direct interactions in complex networks is difficult. In ...
We consider the problem of inferring a causality structure from multiple binary time series by using...