Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 51, No. 3 (2002), pp. 257-280 (24 pages) We model daily catches of fishing boats in the Grand Bank fishing grounds. We use ...

Occupational and Environmental Medicine, Vol. 66, No. 8 (August 2009), pp. 502-508 (7 pages) Objectives: Residual confounding can be present in epidemiological studies because information on ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...

Bayesian networks are powerful tools in probabilistic reasoning, allowing us to model complex systems where uncertainty and causal relationships intertwine. At their core, Bayesian Networks are ...