Species distribution models are generic empirical techniques that have a number of applications. One these applications is to determine which environmental conditions most important for species. The calculation this variable importance depends on assumptions, including the observations used estimate independent each other. Spatial autocorrelation, common feature factors confounds assumption. Be...

Parkinson’s disease (PD) is a neurodegenerative that causes chronic and progressive motor dysfunction. As PD progresses, patients show different symptoms at stages of the disease. The severity assessment inefficient subjective when it comes to artificial diagnosis. However, abnormal gait was contingent subject selection limited. Therefore, few-shot learning based on small sample sets critical s...

Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the piecewise constant sequential importance sampling/resampling ...

We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a “solution” to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors. Ideally the gui...