Search results for "bayesian"
showing 10 items of 604 documents
Comparison of cystatin C and creatinine-based glomerular filtration rate formulas with 51Cr-EDTA clearance in patients with cirrhosis
2011
Renal function is an important predictor of survival in cirrhosis and liver transplantation. GFR estimates using serum cystatin C (CysC) are proposed as better predictors of renal function than ones on the basis of serum creatinine (Cr). Our aims were: (1) evaluate correlations between serum CysC and different methods of creatinine measurements; (2) compare CysC and Cr GFR formulas with (51)Cr-EDTA; and (3) evaluate liver-related parameters potentially influencing GFR.254 blood samples in 65 patients with cirrhosis correlating CysC with four Cr methods were used; another 74 patients comparing (51)Cr-EDTA GFR to Modification of Diet in Renal Disease and Larsson and Hoek formulas for CysC wer…
Copulation duration, but not paternity share, potentially mediates inbreeding avoidance in Drosophila montana
2014
Studying the incidence of inbreeding avoidance is important for understanding the evolution of mating systems, especially in the context of mate choice for genetic compatibility. We investigated whether inbreeding avoidance mechanisms have evolved in the malt fly, Drosophila montana, by measuring mating latency (a measure of male attractiveness), copulation duration, days to remating, offspring production, and the proportion of offspring sired by the first (P1) and second (P2) male to mate in full-sibling and unrelated pairs. SNP markers were used for paternity analysis and for calculating pairwise relatedness values (genotype sharing) between mating pairs. We found 18 % inbreeding depressi…
Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis
2020
The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection tech…
Some contributions in disease mapping modeling
2020
Disease mapping ha recibido un gran interés durante las tres últimas décadas. Esta área de investigación persigue el estudio de la distribución geográfica de eventos relacionados con la salud, tales como la mortalidad o la incidencia de enfermedades, agregados en unidades geográficas, con el fin de identificar principalmente aquellas localizaciones que presentan un mayor riesgo. La aplicación de métodos estadísticos avanzados para llevar a cabo las estimaciones de los riesgos resulta fundamental para obtener estimaciones precisas y profundizar en el entendimiento de la distribución geográfica de las enfermedades. En esta tesis nos centramos en la aplicación y evaluación de varias propuestas…
A Bayesian Multilevel Random-Effects Model for Estimating Noise in Image Sensors
2020
Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This paper presents a Bayesian approach to decomposing and characterizing the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time-series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parame…
Bayesian applications in dynamic econometric models
2009
The purpose of this thesis is to provide a few new ideas to the field of Bayesian econometrics. In particular, the focus of the thesis is on analyzing dynamic econometric models. In the first essay, we provide an easily implementable method for the Bayesian analysis of a simple hybrid DSGE model of Clarida et al. (1999). The forecasting properties of the model are tested against commonly used forecasting tools, such as Bayesian VARs and naïve forecasts based on univariate random walks. In particular, the predictability of three key macroeconomic-variables, inflation, short-term nominal interest rate and a measure of output gap, are studied using quarterly ex post and real-time U.S. data.Our…
Convex semi-infinite games
1986
This paper introduces a generalization of semi-infinite games. The pure strategies for player I involve choosing one function from an infinite family of convex functions, while the set of mixed strategies for player II is a closed convex setC inRn. The minimax theorem applies under a condition which limits the directions of recession ofC. Player II always has optimal strategies. These are shown to exist for player I also if a certain infinite system verifies the property of Farkas-Minkowski. The paper also studies certain conditions that guarantee the finiteness of the value of the game and the existence of optimal pure strategies for player I.
Physics-Aware Gaussian Processes for Earth Observation
2017
Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes …
CiliaCarta: An integrated and validated compendium of ciliary genes
2019
The cilium is an essential organelle at the surface of mammalian cells whose dysfunction causes a wide range of genetic diseases collectively called ciliopathies. The current rate at which new ciliopathy genes are identified suggests that many ciliary components remain undiscovered. We generated and rigorously analyzed genomic, proteomic, transcriptomic and evolutionary data and systematically integrated these using Bayesian statistics into a predictive score for ciliary function. This resulted in 285 candidate ciliary genes. We generated independent experimental evidence of ciliary associations for 24 out of 36 analyzed candidate proteins using multiple cell and animal model systems (mouse…
Evidence for spatiotemporal shift in demersal fishery management priority areas in the western Mediterranean
2022
14 pages, 10 figures, 2 tables, 1 appendix