Researchers develop Bayesian inference for hidden dependence structures in multi-group high-dimensional data
In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing. In such data, it is important to learn the dependent structures connecting the variables and to id...
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In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing. In such data, it is important to learn the dependent structures connecting the variables and to identify a "dependence map" that reveals hidden information in massive data sets.
Originally published at Phys.org
