A recent probabilistic model unified findings on sequential generalization ("rule learning") via independently-motivated principles of generalization (Frank & Tenenbaum, 2011). Endress critiques this work, arguing that learners do not prefer more specific hypotheses (a central assumption of the model), that "common-sense psychology" provides an adequate explanation of rule learning, and that Ba...

Although the sophistication and flexibility of the statistical technology available to the data analyst have increased, some durable, simple principles remain valid. Hypothesis-driven analyses, which were anticipated and specified in the protocol, must still be kept separate and privileged relative to the important, but risky data mining made possible by modern computers. Analyses that have a f...

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function. We propose a new parameter fitting method, minimum probability flow (MPF), which is applicable to any parametric model. We demonstrate parameter estimation using MPF in two cases: a continuous state space model, and an Ising spin glass. In the latter case, MPF outperforms curren...

Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inf...

How are listeners able to identify whether the pitch of a brief isolated sample of an unknown voice is high or low in the overall pitch range of that speaker? Does the speaker's voice quality convey crucial information about pitch level? Results and statistical models of two experiments that provide answers to these questions are presented. First, listeners rated the pitch levels of vowels take...

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fit...

A polymer molecule (represented by a statistical chain) end-grafted to a topologically rough surface was studied by static MC simulations. A modified self-avoiding walk on a cubic lattice was used to model the polymer in an athermal solution. Different statistical models of surface roughness were applied. Conformational entropies of chains attached to uncorrelated Gaussian, Brownian, and fracti...

An increasingly popular theory holds that the mind should be viewed as a near-optimal or rational engine of probabilistic inference, in domains as diverse as word learning, pragmatics, naive physics, and predictions of the future. We argue that this view, often identified with Bayesian models of inference, is markedly less promising than widely believed, and is undermined by post hoc practices ...

We study conditions relating to the impossibility of agreeing to disagree in models of interactive KD45 belief (in contrast to models of S5 knowledge, which are used in nearly all the agreements literature). Agreement and disagreement are studied under models of belief in three broad settings: non-probabilistic decision models, probabilistic belief revision of priors, and dynamic communication ...

Detecting nonlinear correlations between time series presents a hard problem for data analysis. We present a generative statistical modeling method for detecting nonlinear generalized synchronization. Truncated Volterra series are used to approximate functional interactions. The Volterra kernels are modeled as linear combinations of basis splines, whose coefficients are estimated via l(1) and l...