This paper tackles the issue of real-time parametric estimation of a wide class of probability density functions from limited datasets. This type of estimation addresses recent applications that require joint sensing and actuation. The suggested estimator operates in the nonlinear subspace that the parameter space of the distribution creates in the measurement sample space. https://thehookahlabes.shop/product-category/bowl/