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The classical statistical problem typically involves a probability distribution which depends on a number of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members are either unobserved or hypothetical, the latter category being introduced as a means of better explaining the data. Sometimes we are interested in these kinds of variable themselves and sometimes in the parameters of the distribution. Many problems that can be cast into this form are treated. These include: missing data, mixtures, latent variables, time series and social measurement problems. Although all can be accommodated within a Bayesian framework, most are best treated from first principles.

Mathematical statistics. --- Statistics. --- Variables (Mathematics). --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Factor analysis. --- Variables (Mathematics) --- Analysis, Factor --- Factorial analysis --- Statistics, general. --- Mathematical constants --- Multivariate analysis --- Structural equation modeling --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics .

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Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies.

streamflow forecasting --- C-vine copula --- quantile regression --- joint dependencies --- water resource management --- ecological relationship --- factorial analysis --- input-output analysis --- optimal path --- reduction --- urban solid waste system --- desalination --- reverse osmosis --- modelling --- simulation --- parameter estimation --- seawater --- boron --- watershed management --- nonpoint source pollution --- point source pollution --- water quality --- pollutant loadings --- South Texas --- eco-efficiency --- DEA --- CO2 emissions --- forecasting --- ecological indicators --- biomass gasification --- machine learning --- computer modeling --- computer simulation --- regression --- model reduction --- LASSO --- classification --- feature selection --- financial market --- investing --- sustainability --- renewable energy support --- energy modeling --- energy system design --- generation profile --- environmental footprint --- renewable energy --- electricity production --- unlisted companies --- Germany --- feed-in tariff --- biofuel policy --- investment profitability analysis --- the pay-off method --- simulation decomposition --- sourcing --- operational flexibility --- business aviation --- turboprop --- electric motor --- specific power --- Monte Carlo simulation --- Iowa food-energy-water nexus --- nitrogen export --- system modeling --- weather modeling --- optimal allocation --- interval --- fuzzy --- dynamic programming --- water resources --- n/a