Abstract: Systems and methods for modeling complex probability distributions are described. One embodiment includes a method for training a restricted Boltzmann machine (RBM), wherein the method includes generating, from a first set of visible values, a set of hidden values in a hidden layer of a RBM and generating a second set of visible values in a visible layer of the RBM based on the generated set of hidden values. The method includes computing a set of likelihood gradients based on the first set of visible values and the generated set of visible values, computing a set of adversarial gradients using an adversarial model based on at least one of the set of hidden values and the set of visible values, computing a set of compound gradients based on the set of likelihood gradients and the set of adversarial gradients, and updating the RBM based on the set of compound gradients.

Abstract: Systems and methods to account for uncertainties from missing covariates in generative model predictions. One embodiment includes a method for updating the values for uncertainty used in a generative model that is created using a set of known prognostically important baseline data. The method includes steps for determining a value, within the generative model, for the variance in outcome given the known prognostically important baseline data, wherein the steps include imputing values for a set of unknown prognostically important baseline data, and determining estimations for explained and unexplained variance in outcome for each subject when given both sets of data.

Abstract: Systems and methods for designing random control trials in accordance with embodiments of the invention are illustrated. One embodiment includes a method for designing a target random control trial. The method includes steps for generating a set of prognostic scores for a set of samples, computing a first correlation between the set of prognostic scores and a set of outcomes for the set of samples, computing a first variance for the set of outcomes for the set of samples, estimating a second correlation and a second variance for a target random control trial, and determining a set of target trial parameters based on the first and second correlations and the first and second variances.

Abstract: Systems and methods for determining treatment effects of a randomized control trial (RCT) in accordance with embodiments of the invention are illustrated. One embodiment includes a method for determining treatment effects. The method includes steps for receiving data from a RCT, generating result data using a set of one or more generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: Systems and methods for modeling complex probability distributions are described. One embodiment includes a method for training a restricted Boltzmann machine (RBM), wherein the method includes generating, from a first set of visible values, a set of hidden values in a hidden layer of a RBM and generating a second set of visible values in a visible layer of the RBM based on the generated set of hidden values. The method includes computing a set of likelihood gradients based on the first set of visible values and the generated set of visible values, computing a set of adversarial gradients using an adversarial model based on at least one of the set of hidden values and the set of visible values, computing a set of compound gradients based on the set of likelihood gradients and the set of adversarial gradients, and updating the RBM based on the set of compound gradients.