iknyazeva Goto Github PK

bayesian_inference

Bayesian inference for group-level fMRI analysis (based on SPM12 v6906)

bolddeconvolution

eeganalysis

eegprocessing

some function for eeg data processing

inverseproblem

movieemotions

Let's do it)

neurolib

Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿‍🔬

openclosebaseline

Open-Close dataset Baseline

pytorch-deep-learning

Deep Learning (with PyTorch)

resources

PyMC3 educational resources

risktestosterone

solar_flares_forecasting

The sun produces solar flares, which have the power to affect the Earth and near-Earth environment with their great bursts of electromagnetic energy and particles. These flares have the power to blow out transformers on power grids and disrupt satellite systems. There is a long lasting task of predictions such events for minimizing its negative impact. Doing so is a difficult task because of the rarity of these events. The success in this task not changes significantly over the last 60 years. Actually, this was a topic of my Ph.D. research, and I did it without any machine learning. But either in the era of big data, there is no big success in this task. The most common approach described in the paper Bobra et al., 2014. The main drawback of the approach is ignoring time dependence on features. Here I tried to use knowledge about working with time series in features. All data for this project could be downloaded from the data link

topology

wctaskfcsimulation

wsbm