Results

**1 - 7**of**7**### APPROVED FOR PUBLIC RELEASE UNCLASSIFIED

, 2013

"... Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabilities within a corpus of text data. The fundamen-tal assumptions in this scheme are that text is a realisation of a stochastic generative model and that this model is well described by the combinatio ..."

Abstract
- Add to MetaCart

Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabilities within a corpus of text data. The fundamen-tal assumptions in this scheme are that text is a realisation of a stochastic generative model and that this model is well described by the combination of multinomial probability distributions and Dirichlet probability distributions. Various means can be used to solve the Bayesian estimation task arising in LDA. Our formulations of LDA are applied to subject matter expert text data elicited through carefully constructed decision support workshops. In the main these workshops address substantial problems in Australian Defence Capabil-ity. The application of LDA here is motivated by a need to provide insights into the collected text, which is often voluminous and complex in form. Addi-tional investigations described in this report concern questions of identifying and quantifying differences between stake-holder group text written to a com-mon subject matter. Sentiment scores and key-phase estimators are used to indicate stake-holder differences. Some examples are provided using unclassi-fied data.

### SIMULATION IN STATISTICS

"... Simulation has become a standard tool in statistics because it may be the only tool available for analyzing some classes of probabilistic models. We review in this paper simulation tools that have been specifically derived to address statistical challenges and, in particular, recent advances in the ..."

Abstract
- Add to MetaCart

(Show Context)
Simulation has become a standard tool in statistics because it may be the only tool available for analyzing some classes of probabilistic models. We review in this paper simulation tools that have been specifically derived to address statistical challenges and, in particular, recent advances in the areas of adaptive Markov chain Monte Carlo (MCMC) algorithms, and approximate Bayesian calculation (ABC) algorithms. 1

### Some Model-Based and Distance-Based Clustering Methods for Characterization of Regional Ecological Stressor-Response Patterns and Regional Environmental Quality Trends

, 2006

"... We develop statistical methods for evaluation of regional variation of ecological stressorresponse relationships, and regional variation in temporal profiles of water quality, for application to data from monitoring stations on bodies of water. To evaluate regional variation in regression relationsh ..."

Abstract
- Add to MetaCart

(Show Context)
We develop statistical methods for evaluation of regional variation of ecological stressorresponse relationships, and regional variation in temporal profiles of water quality, for application to data from monitoring stations on bodies of water. To evaluate regional variation in regression relationships, we use model-based clustering procedures with class-specific regression models. Units for clustering are taken to be basins, or combinations of basins and ecoregions. We rely on a Bayesian formulation and sample the posterior distribution using a Markov chain Monte Carlo algorithm. Two general approaches to the label-switching problem are considered, each leading to procedures that we apply in data analyses. Two applications are presented. We explore some relationships among priors with a Dirichlet distribution for class probabilities. We compare two rank-based criteria for grouping stations according to similarities in temporal profiles. The two criteria are illustrated in a hierarchical cluster analysis based on measurements of a water quality variable. Acknowledgements

### unknown title

"... Summary In this paper we describe the theoretical properties of wavelet based random densities and present algorithms for their generation. We exhibit random densities subject to some standard constraints: smoothness, symmetry, unimodality, and skewness. We also provide three relevant applications o ..."

Abstract
- Add to MetaCart

Summary In this paper we describe the theoretical properties of wavelet based random densities and present algorithms for their generation. We exhibit random densities subject to some standard constraints: smoothness, symmetry, unimodality, and skewness. We also provide three relevant applications of wavelet based-random densities.

### Submitted APPROXIMATION OF IMPROPER PRIOR BY VAGUE

"... Abstract. We propose a convergence mode for prior distributions which allows a sequence of probability measures to have an improper limiting measure. We define a sequence of vague priors as a sequence of probability measures that converges to a non-informative prior. We consider some cases where vag ..."

Abstract
- Add to MetaCart

Abstract. We propose a convergence mode for prior distributions which allows a sequence of probability measures to have an improper limiting measure. We define a sequence of vague priors as a sequence of probability measures that converges to a non-informative prior. We consider some cases where vague priors have necessarily large variances and other cases where they have not. We give some con-structions of vague priors that approximate the Haar measures or the Jeffreys priors. Then, we study the consequences of the convergence of prior distributions on the posterior analysis. We also revisit the Jeffreys-Lindley paradox.

### A Bayesian Approach to Broad-Area Nuclear and Radiological Search Operations

, 2014

"... This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been ..."

Abstract
- Add to MetaCart

(Show Context)
This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been

### How Well Do the Models Forecast?1

, 2005

"... Stocks have reached what looks like a permanently high plateau. ..."