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Tackling the subsampling problem to infer collective properties from limited data
Despite the development of large-scale data-acquisition techniques, experimental
observations of complex systems are often limited to a tiny fraction of the system under …
observations of complex systems are often limited to a tiny fraction of the system under …
Objective Bayesian edge screening and structure selection for Ising networks
The Ising model is one of the most widely analyzed graphical models in network
psychometrics. However, popular approaches to parameter estimation and structure …
psychometrics. However, popular approaches to parameter estimation and structure …
A unifying framework for mean-field theories of asymmetric kinetic Ising systems
Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of
complex systems. As their behavior is not tractable for large networks, many mean-field …
complex systems. As their behavior is not tractable for large networks, many mean-field …
Efficient Bayesian inference of sigmoidal Gaussian Cox processes
We present an approximate Bayesian inference approach for estimating the intensity of a
inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian …
inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian …
Efficient inference for dynamic flexible interactions of neural populations
Hawkes process provides an effective statistical framework for analyzing the interactions of
neural spiking activities. Although utilized in many real applications, the classic Hawkes …
neural spiking activities. Although utilized in many real applications, the classic Hawkes …
Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains
Probing the architecture of neuronal circuits and the principles that underlie their functional
organization remains an important challenge of modern neurosciences. This holds true, in …
organization remains an important challenge of modern neurosciences. This holds true, in …
Multi-class Gaussian process classification made conjugate: Efficient inference via data augmentation
We propose a new scalable multi-class Gaussian process classification approach building
on a novel modified softmax likelihood function. The new likelihood has two benefits: it leads …
on a novel modified softmax likelihood function. The new likelihood has two benefits: it leads …
Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter
For performing regression tasks involved in various physics problems, enhancing the
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …
Efficient inference of flexible interaction in spiking-neuron networks
Hawkes process provides an effective statistical framework for analyzing the time-dependent
interaction of neuronal spiking activities. Although utilized in many real applications, the …
interaction of neuronal spiking activities. Although utilized in many real applications, the …
Nonstationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
High-dimensional classification and feature selection tasks are ubiquitous with the recent
advancement in data acquisition technology. In several application areas such as biology …
advancement in data acquisition technology. In several application areas such as biology …