'Prior distribution' presentation slideshows

Yellowfin Tuna 1975-2005

Yellowfin Tuna 1975-2005. Major Changes. Catch, effort, and length-frequency data for the surface fisheries have been updated to include new data for 2005 and revised data for 1975-2004.

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Measuring Isoform Expression from RNA-Seq data Based on LDA

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Putting the Novelty Back in Phase II Trials

Putting the Novelty Back in Phase II Trials. Elizabeth Garrett-Mayer, PhD Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University AACR/ASCO Workshop Methods in Clinical Cancer Research July 2006. Outline of Talk. Goals of Phase II study Single arm studies

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Topic Discussion: Bayesian Analysis of Informative Hypotheses

Topic Discussion: Bayesian Analysis of Informative Hypotheses. Quantitative Methods Forum 20 January 2014. Outline. Articles: A gentle introduction to Bayesian analysis: Applications to developmental research

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Differential Expressions Bayesian Techniques

Differential Expressions Bayesian Techniques. Lecture Topic 8. Why Bayes ?. A friend of mine who is Bayesian said the following when asked this question: Some problems very hard to solve by classical techniques e.g. Behrens-Fisher problem Every new problem requires a new solution

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Profile Hidden Markov Models

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Specification of a CRM model

Specification of a CRM model. Ken Cheung Department of Biostatistics, Columbia University (joint work with Shing Lee @ Columbia). Agenda. Motivation Components of a CRM model Dose-toxicity function Initial guesses of DLT rates Prior distribution of model parameter

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Bringing Inverse Modeling to the Scientific Community

Bringing Inverse Modeling to the Scientific Community. Hydrologic Data and the Method of Anchored Distributions (MAD). Matthew Over 1 , Daniel P. Ames 2 , & Yoram Rubin 1 1. Department of Civil and Environmental Engineering, University of California, Berkeley

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Accurate Statutory Valuation

Accurate Statutory Valuation. JOHN MacFARLANE University of Western Sydney. Content. Motivation Methodology Examples. Motivation. Much of estimation theory is focussed on (obsessed with?) unbiasedness There are many situation where unbiased estimation is not relevant: Appointments;

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Stephen Mildenhall

Stephen Mildenhall. Bayesian-Bootstrap Loss Development CAS DFA Seminar Chicago, July 1999. 1. Model Example Analogy Unobservable Qty Claim Freq ? Ultimate Loss U Prior Distribution ? ~ ?(?,?) U ~ g U ( u ) Observable Qty Number of claims N Loss at n th report L

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II .2 Statistical Inference: Sampling and Estimation

II .2 Statistical Inference: Sampling and Estimation. A statistical model ? is a set of distributions (or regression functions ), e.g., all uni -modal, smooth distributions.

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Point and Interval Estimates

Point and Interval Estimates. Examples with z and t distributions Single sample; two samples Result: Sums (and differences) of normally distributed RV are normally distributed. Determining the variance of the difference between means for two independent samples

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Automatic Metaphor Interpretation as a Paraphrasing Task

Automatic Metaphor Interpretation as a Paraphrasing Task. Ekaterina Shutova Computer Lab, University of Cambridge NAACL 2010. Outline. What is metaphor The idea and overview of the system Experimental data Method Evaluation Conclusion. Metaphor in this paper.

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WHY MAKING BAYESIAN NETWORKS BAYESIAN MAKES SENSE.

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Objectives: Example Clustered Transformations MAP Adaptation

LECTURE 19: PRACTICAL ISSUES IN MLLR. Objectives: Example Clustered Transformations MAP Adaptation Resources: ECE 7000: MLLR MG: MLLR Transformations TAM: Adaptation for ASR ECE 8463: Adaptation AM: Transform Sharing.

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Stacked RBMs

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Example

Example. Human males have one X-chromosome and one Y-chromosome, while females have two X-chromosomes each chromosome being inherited from one parent. Hemophilia is a disease that exhibits X-chromosome-linked recessive inheritance, meaning that a male who inherits the gene that

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Stage 2 Presentation MAS2317 Katie Allman

Stage 2 Presentation MAS2317 Katie Allman.

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Hyperparameter Estimation for Speech Recognition Based on Variational Bayesian Approach

20.4. The value of F. Phoneme Acc. (%). 20.3. 20.2. : Root node. mean vector. 0. 2. 4. 6. 8. 10. T. T. : Root node. 20,000 sentences. 20,000 sentences. covariance matrix. 79. : Variational posterior. ,. 78. 77. 76. 75. 0. 2. 4. 6. 8. 10. P. F. : Leaf node index.

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