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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)

- Dan Lovell, Ryan P Adams, Vikash K Mansinghka
- 2012

The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian non-parametric 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 varia-tional methods or Markov chain Monte… (More)

Most natural domains can be represented in multiple ways: animals may be thought of in terms of their tax-onomic groupings or their ecological niches and foods may be thought of in terms of their nutritional content or social role. We present a computational framework that discovers multiple systems of categories given information about a domain of objects… (More)

- Noah D Goodman, Chris L Baker, Elizabeth Baraff Bonawitz, Vikash K Mansinghka, Alison Gopnik, Henry Wellman +2 others
- 2006

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)

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely… (More)

- Daniel M Oftechnology, Roy, Leslie P Kaeaing, Daniel M Roy, Noah Goodman, Vikash Mansinghka +29 others
- 2011

We investigate the class of computable probability distributions and explore the fundamental limitations of using this class to describe and compute conditional distributions. In addition to proving the existence of noncomputable conditional distributions, and thus ruling out the possibility of generic probabilistic inference algorithms (even inefficient… (More)

We address the problem of learning grounded causal models: systems of concepts that are connected by causal relations and explicitly grounded in perception. We present a Bayesian framework for learning these models—both a causal Bayesian network structure over variables and the consequential region of each variable in perceptual space—from dynamic… (More)

People have strong intuitions about the masses of objects and the causal forces that they exert upon one another. These intuitions have been explored through a variety of tasks, in particular judging the relative masses of objects involved in collisions and evaluating whether one object caused another to move. We present a single framework for explaining… (More)

- Vikash K Mansinghka, Eric M Jonas, Joshua B Tenenbaum, Vikash Mansinghka, Eric Jonas, Josh Tenenbaum
- 2008

We introduce combinational stochastic logic, an abstraction that generalizes deterministic digital circuit design (based on Boolean logic gates) to the probabilistic setting. We show how this logic can be combined with techniques from contemporary digital design to generate stateless and stateful circuits for exact and approximate sampling from a range of… (More)

We use Church, a Turing-universal language for stochastic generative processes and the probability distributions they induce, to study and extend several objects in nonparametric Bayesian statistics. We connect exchangeability and de Finetti measures with notions of purity and closures from functional programming. We exploit delayed evaluation to provide… (More)