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Found 3 papers, 2 papers with code
Particle gradient descent model for point process generation
This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window.
Statistical learning of geometric characteristics of wireless networks
To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation.
Determinantal thinning of point processes with network learning applications
A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques.
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