Add to ORKGThe goodness of a predictive distribution depends on the aim of the prediction. This presentation intends to shed light on properties of predictive distributions in use nowadays. We also propose a new predictive distribution that may be useful to obtain calibrated predictions for the probabilities of a future random variable of interest. This predictive distribution can be easily computed by a simple bootstrap procedure. In order to compare the different predictive distributions, some simulation studies are also presented
This paper deals with simultaneous prediction for time series models. In particular, it presents a s...
In the last 30 years, whilst there has been an explosion in our ability to make quantative predictio...
Can we forecast the probability of an arbitrary sequence of events happening so that the stated prob...
The goodness of a predictive distribution depends on the aim of the prediction. This presentation i...
In this short paper we propose the use of a calibration procedure in order to obtain predictive pr...
The specification of multivariate prediction regions, having coverage probability closed to the targ...
This paper concerns prediction from the frequentist point of view. The aim is to define a well-calib...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
Forecasts of probability distributions are needed to support decision making in many applications. T...
Predictions and forecasts of machine learning models should take the form of probability distributio...
Prediction, where observed data is used to quantify uncertainty about a future observation, is a fun...
We argue that prediction intervals based on predictive likelihood do not correct for curvature with ...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Th...
We are interested in the question of how to learn rules, when those rules make probabilistic stateme...
This paper deals with simultaneous prediction for time series models. In particular, it presents a s...
In the last 30 years, whilst there has been an explosion in our ability to make quantative predictio...
Can we forecast the probability of an arbitrary sequence of events happening so that the stated prob...
The goodness of a predictive distribution depends on the aim of the prediction. This presentation i...
In this short paper we propose the use of a calibration procedure in order to obtain predictive pr...
The specification of multivariate prediction regions, having coverage probability closed to the targ...
This paper concerns prediction from the frequentist point of view. The aim is to define a well-calib...
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable ...
Forecasts of probability distributions are needed to support decision making in many applications. T...
Predictions and forecasts of machine learning models should take the form of probability distributio...
Prediction, where observed data is used to quantify uncertainty about a future observation, is a fun...
We argue that prediction intervals based on predictive likelihood do not correct for curvature with ...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Th...
We are interested in the question of how to learn rules, when those rules make probabilistic stateme...
This paper deals with simultaneous prediction for time series models. In particular, it presents a s...
In the last 30 years, whilst there has been an explosion in our ability to make quantative predictio...
Can we forecast the probability of an arbitrary sequence of events happening so that the stated prob...