1,721,691 research outputs found
Walker, Stephen, TX3877
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/423513Surname: WALKER. Given Name(s) or Initials: STEPHEN. Military Service Number or Last Known Location: TX3877. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 23923.250028
Item: [2016.0049.55774] "Walker, Stephen, TX3877
[The walker, Stephen Maclean, Kings Cross, Sydney, 2000] [picture] /
No full textCondition: good.; Title devised by cataloguer based on information from acquisition file number 204/20/00161.; Part of the collection of photographs about homeless people in Kings Cross by Roslyn Sharp. Kings Cross, Sydney. The walker. Stephen Maclean lives at the fringe of the Cross. He wrote the books "The Boy from Oz" and "Starstruck" and has a manuscript in progress on his friend, the American actress Peggy Lee. Photo taken in 2000 in Stephen's apartment, while he listens to music
Bayesian Estimation of the Discrepancy with Misspecified Parametric Models
Full text linkWe study a Bayesian model where we have made specific requests about the parameter values to be estimated. The aim is to find the parameter of a parametric family which minimizes a distance to the data generating density and then to estimate the discrepancy using nonparametric methods. We illustrate how coherent updating can proceed given that the standard Bayesian posterior from an unidentifiable model is inappropriate. Our updating is performed using Markov Chain Monte Carlo methods and in particular a novel method for dealing with intractable normalizing constants is required. Illustrations using synthetic data are provided.European Research Council (ERC) through StG "N-BNP" 306406Regione PiemonteMathematic
A Nonparametric Model for Stationary Time Series
Full text linkStationary processes are a natural choice as statistical models for time series data, owing to their good estimating properties. In practice, however, alternative models are often proposed that sacrifice stationarity in favour of the greater modelling flexibility required by many real-life applications. We present a family of time-homogeneous Markov processes with nonparametric stationary densities, which retain the desirable statistical properties for inference, while achieving substantial modelling flexibility, matching those achievable with certain non-stationary models. A latent extension of the model enables exact inference through a trans-dimensional Markov chain Monte Carlo method. Numerical illustrations are presented
Bayesian Consistency for Markov Models
Full text linkWe consider sufficient conditions for Bayesian consistency of the transition density of time homogeneous Markov processes. To date, this remains somewhat of an open problem, due to the lack of suitable metrics with which to work. Standard metrics seem inadequate, even for simple autoregressive models. Current results derive from generalizations of the i.i.d. case and additionally require some non-trivial model assumptions. We propose suitable neighborhoods with which to work and derive sufficient conditions for posterior consistency which can be applied in general settings. We illustrate the applicability of our result with some examples; in particular, we apply our result to a general family of nonparametric time series models.We consider sufficient conditions for Bayesian consistency of the transition density of time homogeneous Markov processes. To date, this remains somewhat of an open problem, due to the lack of suitable metrics with which to work. Standard metrics seem inadequate, even for simple autoregressive models. Current results derive from generalizations of the i.i.d. case and additionally require some non-trivial model assumptions. We propose suitable neighborhoods with which to work and derive sufficient conditions for posterior consistency which can be applied in general settings. We illustrate the applicability of our result with some examples; in particular, we apply our result to a general family of nonparametric time series models
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