Abstract: Recently, the information bottleneck method, a machine learning framework, was incorporated in several communication engineering related applications. However, most of these applications are ...
Abstract: The problem of quantizing two-dimensional Gaussian random variables is considered. It is shown that, for all but a finite number of cases, a polar ...
A number of special representations are considered for the joint distribution of qualitative, mostly binary, and quantitative variables. In addition to the conditional Gaussian models and to ...
There was an error while loading. Please reload this page. There are two examples of the conditional Gaussian distribution with Python (Jupyter Notebook) code ...
CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...
Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of ...
Gaussian Processes: A Complete Guide Over the past week, I’ve dedicated nearly 2 hours each day to deeply understand Gaussian Processes (GPs) — not just how to apply them, but to build an intuitive ...
Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse ...
We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced ...
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