Bayesian methodsspatial distributionmeasurement errorThe widespread use of line-transect surveys to estimate the abundance of ecological populations has seen a number of new developments in methodology. For example, nonparametric approaches have been introduced to estimate detection probabilities for objects ...
Moreover, from a teaching perspective, introductions to Bayesian statisticsif they are given at allare circumscribed by these apparent calculational difficulties. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily ...
Bayesian statistics with- out tears:a sampling-resampling perspective. The American Statistician, 46:84-88, 1992.Smith, A. F. M. & Gelfand, A. E. (1992). Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician, 46, 84-88....
Department of Statistics, University of New South Wales, Sydney, NSW, 2052, Australia David J. Nott ) Cite this article Nott, D.J., Kuk, A.Y.C. & Duc, H. Efficient sampling schemes for Bayesian MARS models with many predictors.Stat Comput15...
et al. Bayesian statistics and modelling. Nat. Rev. Methods Primers 1, 1 (2021). Google Scholar D’Agostini, G. Bayesian Reasoning In Data Analysis: A Critical Introduction (World Scientific, 2003). Gregory, P. Bayesian Logical Data Analysis for the Physical Sciences (Cambridge Univ. Press...
Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover high-level properties of the data, such as which inputs are relevant to predicting the response.Radford M. Neal...
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods global-optimizationgaussian-processeslatin-hypercube-samplingsurrogate-modelsmixture-of-experts UpdatedMar 23, 2025 Rust sahilm89/lhsmdu ...
1 Keywords: Bayesian ?ltering, nonlinear non-Gaussian state space models, sequential Monte Carlo methods, importance sampling, Rao-Blackwellised estimates I. Introduction Many problems in applied statistics, statistical signal processing, time series analysis and econometrics can be stated in a state ...
A generalization of sampling without replacement from a finite universe. Introduction to Distance Sampling: Estimating Abundance of Biology Populations On sequential Monte Carlo sampling methods for Bayesian filtering
Bayesian analysis of bilinear time series models: a Gibbs sampling approach. Communications in Statistics--Theory and Methods 21: 3407-3425.Chen, C.W.S. (1992): Bayesian Analysis of Bilinear Time Series Models: A Gibbs Sampling Approach. Communications in Statistics: Theory and Methods 21, 3407...