Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical a...

Ongoing developments in neural network models are continually advancing the state-of-the-art in terms of system accuracy. However, the predicted labels should not be regarded as the only core output; also important is a well calibrated estimate of the prediction uncertainty. Such estimates and their calibration is critical in relation to robust handling of out of distribution events not observe...

The surface growing framework presented by Besl and Jain [2] has served as the basis for many range segmentation techniques. It has been augmented with alternative fitting techniques [17], model selection criteria [11, 15], and solid modelling components [6]. All of these surface growing approaches, however, require global thresholds. Range scenes typically cannot satisfy the global threshold a...

Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccu...

1 2 A major focus for transportation safety analysts is the development of crash prediction models, a 3 task for which an extremely wide selection of model types are available. Perhaps the most common 4 crash prediction model is the negative binomial (NB) regression model. The NB model gained 5 popularity due to its relative ease of implementation and its ability to handle overdispersion in 6 c...

Given time series data X1, . . . , Xn, the problem of optimal prediction of Xn+1 has been well-studied. The same is not true, however, as regards the problem of constructing a prediction interval with prespecified coverage probability for Xn+1, i.e., turning the point predictor into an interval predictor. In the past, prediction intervals have mainly been constructed for time series that obey a...

Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccu...

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Baye...

Using a conditional method, explicit formulae for computing quantiles pertinent to prediction intervals for future Weibull order statistics are developed for two cases: when only previous independent failure data are available, and when both previous independent failure data and early-failure data in current experiment are available. The second case includes the case when only current early-fai...