Asymmetric confidence intervals reveal hidden information in working memory

Confidence Intervals for Long Memory Regressions

Confidence Intervals for the Mutual Information

”THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD” By combining a bound on the absolute value of the difference of mutual information between two joint probability distributions with a fixed variational distance, and a bound on the probability of a maximal deviation in variational distance between a true joint probability distribution and an empirical joint probability distribution, confidenc...

Confidence Intervals for Information Retrieval Evaluation

Information retrieval results are currently limited to the publication in which they exist. Significance tests are used to remove the dependence of the evaluation on the query sample, but the findings cannot be transferred to other systems not involved in the test. Confidence intervals for the population parameters provide query independent results and give insight to how each system is expecte...

On Increasing Confidence in Confidence Intervals

A set of N data elements (xI +•.• + X~)/N and variance of the th The r moment of the data elements elements is used to estimate E(xr ). th r x1' •.•• xN has r moment E(x)= th r 2r 2 r r moment Var(x ) = E(x )-E (x ). in an arbitrary subset of k Over all the choices of the sample, the mean error is zero and the mean squared error N-k r k(N-I) Var(x). A little-known theorem by Marlow shows that t...

Estimating Information Rates with Confidence Intervals in Neural Spike Trains

Information theory provides a natural set of statistics to quantify the amount of knowledge a neuron conveys about a stimulus. A related work (Kennel, Shlens, Abarbanel, & Chichilnisky, 2005) demonstrated how to reliably estimate, with a Bayesian confidence interval, the entropy rate from a discrete, observed time series. We extend this method to measure the rate of novel information that a neu...