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      Impact of Tanzania’s Wildlife Management Areas on household wealth

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          Stan: A Probabilistic Programming Language

          Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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            Categorical Data Analysis

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              Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

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                Author and article information

                Contributors
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                Journal
                Nature Sustainability
                Nat Sustain
                Springer Science and Business Media LLC
                2398-9629
                March 2020
                December 23 2019
                : 3
                : 3
                : 226-233
                Article
                10.1038/s41893-019-0458-0
                80b9361f-7110-4ddc-929a-c93884207c8e
                © 2019

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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