Real-time adaptive track generation in racing games

Real-time Adaptive Track Generation in Racing Game

Maximum likelihood and pseudo score approaches for parametric time-to-event analysis with informative entry times

We develop a maximum likelihood estimating approach for time-to-event Weibull regression models with outcome-dependent sampling, where sampling of subjects is dependent on the residual fraction of the time left to developing the event of interest. Additionally, we propose a two-stage approach which proceeds by iteratively estimating, through a pseudo score, the Weibull parameters of interest (i.e., the regression parameters) conditional on the inverse probability of sampling weights; and then re-estimating these weights (given the updated Weibull parameter estimates) through the profiled full likelihood. With these two new methods, both the estimated sampling mechanism parameters and the Weibull parameters are consistently estimated under correct specification of the conditional referral distribution. Standard errors for the regression parameters are obtained directly from inverting the observed information matrix in the full likelihood specification and by either calculating bootstrap or robust standard errors for the hybrid pseudo score/profiled likelihood approach. Loss of efficiency with the latter approach is considered. Robustness of the proposed methods to misspecification of the referral mechanism and the time-to-event distribution is also briefly examined. Further, we show how to extend our methods to the family of parametric time-to-event distributions characterized by the generalized gamma distribution. The motivation for these two approaches came from data on time to cirrhosis from hepatitis C viral infection in patients referred to the Edinburgh liver clinic. We analyze these data here.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS725 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Lost in America: Helping Your Friends Find Their Way Home

Tom Clegg and Warren Bird have teamed together to write a thought provoking new book Lost in America. Chapter Two of their new book is excerpted here and presents “Seven Deadly Statistics” that will cause most of us to think twice about what is taking place in evangelism in North America

Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Practical Lossless Compression with Latent Variables using Bits Back Coding

Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner. We present `Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate. We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE. Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time. We make our implementation available open source at https://github.com/bits-back/bits-back

Detecting the Gravitational Redshift of Cluster Gas

We examine the gravitational redshift of radiation emitted from within the potential of a cluster. Spectral lines from the intracluster medium (ICM) are redshifted in proportion to the emission-weighted mean potential along the line of sight, amounting to approximately 50 km/s at a radius of 100 kpc/h, for a cluster dispersion of 1200 km/s. We show that the relative redshifts of different ionization states of metals in the ICM provide a unique probe of the three-dimensional matter distribution. An examination of the reported peculiar velocities of cD galaxies in well studied Abell clusters reveals they are typically redshifted by an average of $\sim +200$ km/s. This can be achieved by gravity with the addition of a steep central potential associated with the cD galaxy. Note that in general gravitational redshifts cause a small overestimate of the recessional velocities of clusters by an average of $\sim$ 20 km/s.Comment: 6 pages, 3 figures, accepted to the Astrophysical Journal Letter

Spread Divergences

For distributions p and q with different supports, the divergence D(p|q) may not exist. We define a spread divergence on modified p and q and describe sufficient conditions for the existence of such a divergence. We demonstrate how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread. We also give examples of using a spread divergence to train and improve implicit generative models, including linear models (Independent Components Analysis) and non-linear models (Deep Generative Networks)