Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
Keyword(s):
AbstractMixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.
2018 ◽
Keyword(s):
2000 ◽
Vol 25
(2)
◽
pp. 101-132
◽
2020 ◽
Vol 34
(07)
◽
pp. 11029-11036
2020 ◽
Vol 34
(07)
◽
pp. 11507-11514