Human Inferences about Sequences: A Minimal Transition Probability Model

Human Inferences about Sequences: A Minimal Transition Probability Model

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothes...

Microsatellite mutations and inferences about human demography.

Microsatellites have been widely used as tools for population studies. However, inference about population processes relies on the specification of mutation parameters that are largely unknown and likely to differ across loci. Here, we use data on somatic mutations to investigate the mutation process at 14 tetranucleotide repeats and carry out an advanced multilocus analysis of different demogr...

Inferences about human demography based on multilocus analyses of noncoding sequences.

Data from 10 unlinked autosomal noncoding regions, resequenced in 15 individuals from each of three populations, were used in a multilocus analysis to test models of human demography. Each of the 10 regions consisted of approximately 2500 bp. The multilocus analysis, based on summary statistics (average and variance of Tajima's D and Fu and Li's D*), was used to test a family of models with rec...

Inferences About the Components of a Generalized Additive Model

A Simple-Transition Model for Relational Sequences

We use “nearly sound” logical constraints to infer hidden states of relational processes. We introduce a simple-transition cost model, which is parameterized by weighted constraints and a statetransition cost. Inference for this model, i.e. finding a minimum-cost state sequence, reduces to a single-state minimization (SSM) problem. For relational Horn constraints, we give a practical approach t...