Forecast ensembles typically show a spread-skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the Spatial BMA technique, which combines BMA an...

BACKGROUND An in vivo animal study and a prospective clinical study have indicated that bone marrow aspirate (BMA) augments spinal arthrodesis. However, there is no quantified data to explain why fusion rate can be augmented by BMA in lumbar posterolateral fusion. METHODS To analyze the proportion of mesenchymal stem cells (MSCs) and osteogenic factors in human BMA and peripheral blood (PB) o...

The Rate Constrained Block Matching Algorithm, (RC-BMA), introduced in this paper jointly minimizes DFD (Displaced Frame Diierence) variance and entropy or conditional entropy of motion vectors for determining the motion vectors in low rate video coding applications where the contribution of the motion vector rate to the overall coding rate might be signiicant. The motion vector rate vs. DFD va...

Ensembles used for probabilistic weather forecasting often exhibit a spread-skill relationship, but they tend to be underdispersive. This paper proposes a principled statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF...

Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the bias-corrected ensemble members, where the weights reflect the relative skill of the individual members over a training period. This wor...

The Rate Constrained Block Matching Algorithm, (RC-BMA), introduced in this paper, jointly minimizes Displaced Frame Di erence (DFD) variance and entropy or conditional entropy of motion vectors for determining the motion vectors. It is intended for use in low rate video coding applications where the contribution of the motion vector rate to the overall coding rate might be signi cant. The DFD ...

The authors introduce two ways to produce locally calibrated grid-based probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias and variance across the domain, and modify it to make it local. The first local method, geostatistical model averaging (GMA), computes the predictive ...