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Objective: We predicted that accelerometry would be a viable alternative to electromyography (EMG) for assessing fundamental Transcranial Magnetic Stimulation (TMS) measurements (e.g. Resting Motor Threshold (RMT), recruitment curves, latencies). New Metho ...
Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications ...
In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...
In visual crowding, the presence of neighboring elements impedes the perception of a target. Crowding is traditionally explained with feedforward, local models. However, increasing the number of neighboring elements can decrease crowding, i.e., lead to unc ...
A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors ...
Previous research reported that corvids preferentially cache food in a location where no food will be available or cache more of a specific food in a location where this food will not be available. Here, we consider possible explanations for these prospect ...
This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingerma ...
Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...
Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...
This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likeliho ...
