Displaying similar documents to “Generalised filtering.”

A Bayesian Spatial Mixture Model for FMRI Analysis

Geliazkova, Maya (2010)

Serdica Journal of Computing

Similarity:

We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics...

Stochastic Inverse Problem with Noisy Simulator. Application to aeronautical model

Nabil Rachdi, Jean-Claude Fort, Thierry Klein (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

Similarity:

Inverse problem is a current practice in engineering where the goal is to identify parameters from observed data through numerical models. These numerical models, also called Simulators, are built to represent the phenomenon making possible the inference. However, such representation can include some part of variability or commonly called uncertainty (see [4]), arising from some variables of the model. The phenomenon we study is the...

An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study

Ewa Skotarczak, Ewa Bakinowska, Kamila Tomaszyk (2014)

Biometrical Letters

Similarity:

A nonlinear statistical approach was used to evaluate the efficiency of plant protection products. The methodology presented can be implemented when the observations in an experiment are recorded as success or failure. This occurs, for example, when following the application of a herbicide or pesticide, a single weed or insect is classified as alive (failure) or dead (success). Then a higher probability of success means a higher efficiency of the tested product. Using simulated data...

Bayesian methods in hydrology: a review.

David Ríos Insua, Raquel Montes Díez, Jesús Palomo Martínez (2002)

RACSAM

Similarity:

Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.

Bayesian joint modelling of the mean and covariance structures for normal longitudinal data.

Edilberto Cepeda-Cuervo, Vicente Nunez-Anton (2007)

SORT

Similarity:

We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We propose a new and computationally efficient classic estimation method based on the Fisher scoring algorithm to obtain the maximum likelihood estimates of the parameters. In addition, we also propose a new and innovative Bayesian methodology based on the Gibbs sampling, properly adapted for...

Stochastic models and statistical inference for plant pollen dispersal

Catherine Laredo, Agnès Grimaud (2007)

Journal de la société française de statistique

Similarity:

Modelling pollen dispersal is essential to make predictions of cross-pollination rates in various environmental conditions between plants of a cultivated species. An important tool for studying this problem is the “individual pollen dispersal function” or “kernel dispersal”. Various models for airborne pollen dispersal are developed. These models are based on assumptions about wind directionality, gravity, settling velocity and may integrate other biological or external parameters. Some...

Bayesian inference in applied statistics.

Arthur P. Dempster (1980)

Trabajos de Estadística e Investigación Operativa

Similarity:

The task of assessing posterior distributions from noisy empirical data imposes difficult requirements of modelling, computing and assessing sensitivity to model choice. Seasonal analysis of economic time series is used to illustrate ways of approaching such difficulties.