Vikash K. Mansinghka

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Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of(More)
We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture(More)
We propose a causal Bayesian model of false belief reasoning in children. This model realizes theory of mind as the rational use of intuitive theories and supports causal prediction, explanation, and theory revision. The model undergoes an experience-driven false belief transition. We investigate the relationship between prediction, explanation, and(More)
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. In most applications, however, the Dirichlet process requires approximate inference to be performed with variational methods or Markov chain Monte(More)
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that(More)
The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of features and relations defined over a set of objects, an annotated hierarchy includes a specification of the categories that are most useful for describing each individual feature and relation. We define a(More)
People have strong intuitions about the influence objects exert upon one another when they collide. Because people's judgments appear to deviate from Newtonian mechanics, psychologists have suggested that people depend on a variety of task-specific heuristics. This leaves open the question of how these heuristics could be chosen, and how to integrate them(More)
Traditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in. Here we capture this form of prior(More)
Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or “analysis-by-synthesis” approaches, can capture rich scene structure but have been less widely applied than their discriminative(More)
We introduce adaptive sequential rejection sampling, an algorithm for generating exact samples from high-dimensional, discrete distributions, building on ideas from classical AI search. Just as systematic search algorithms like A* recursively build complete solutions from partial solutions, sequential rejection sampling recursively builds exact samples over(More)