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    <title>The feel-good open science story versus the preregistration (who do you think wins?) | Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2024-09-24T13:25:53+00:00</dc:date>
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    <dc:creator>amy</dc:creator><dc:subject>science statistics</dc:subject>
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    <title>rmcelreath/rethinking: Statistical Rethinking course and book package</title>
    <dc:date>2022-07-22T22:58:07+00:00</dc:date>
    <link>https://github.com/rmcelreath/rethinking</link>
    <dc:creator>amy</dc:creator><description><![CDATA[This R package accompanies a course and book on Bayesian data analysis: McElreath 2020. Statistical Rethinking, 2nd edition, CRC Press. If you are using it with the first edition of the book, please see the notes at the bottom of this file.

It contains tools for conducting both quick quadratic approximation of the posterior distribution as well as Hamiltonian Monte Carlo (through RStan or cmdstanr - mc-stan.org). Many packages do this. The signature difference of this package is that it forces the user to specify the model as a list of explicit distributional assumptions. This is more tedious than typical formula-based tools, but it is also much more flexible and powerful and---most important---useful for teaching and learning. When students have to write out every detail of the model, they actually learn the model.]]></description>
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    <title>Flipping USB Connectors. Bayesian decision analysis shows why… | by Allen Downey | Jun, 2021 | Towards Data Science</title>
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    <title>Nextstrain Cov</title>
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    <title>Coronavirus Update (Live): 73,439 Cases and 1,875 Deaths from COVID-19 Wuhan China Virus Outbreak - Worldometer</title>
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    <title>Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/Ch1_Introduction_TFP.ipynb at master · CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers · GitHub</title>
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    <dc:creator>amy</dc:creator><description><![CDATA[tfp port]]></description>
<dc:subject>TensorFlow tfp statistics machine_learning</dc:subject>
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    <title>Bayesian Methods for Hackers</title>
    <dc:date>2019-03-07T17:35:23+00:00</dc:date>
    <link>https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#tensorflow</link>
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    <title>(2) TensorFlow Probability: Learning with confidence (TF Dev Summit '19) - YouTube</title>
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<item rdf:about="http://ww2.amstat.org/meetings/sdss/2019/">
    <title>2019 Symposium on Data Science and Statistics</title>
    <dc:date>2019-02-11T23:23:14+00:00</dc:date>
    <link>http://ww2.amstat.org/meetings/sdss/2019/</link>
    <dc:creator>amy</dc:creator><dc:subject>data_science statistics moi</dc:subject>
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<item rdf:about="https://github.com/tensorflow/probability">
    <title>tensorflow/probability: Probabilistic reasoning and statistical analysis in TensorFlow</title>
    <dc:date>2018-08-09T17:05:04+00:00</dc:date>
    <link>https://github.com/tensorflow/probability</link>
    <dc:creator>amy</dc:creator><description><![CDATA[TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.]]></description>
<dc:subject>TensorFlow statistics machine_learning probability</dc:subject>
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<dc:identifier>https://pinboard.in/u:amy/b:50e8d926c834/</dc:identifier>
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<item rdf:about="http://andrewgelman.com/2018/05/22/statistical-significance-filter-leads-overoptimistic-expectations-replicability/">
    <title>The statistical significance filter leads to overoptimistic expectations of replicability - Statistical Modeling, Causal Inference, and Social Science</title>
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    <dc:creator>amy</dc:creator><description><![CDATA[Treating a result as publishable just because the p-value is less than 0.05 leads to overoptimistic expectations of replicability. These overoptimistic expectations arise due to Type M(agnitude) error: when underpowered studies yield significant results, effect size estimates are guaranteed to be exaggerated and noisy. These effects get published, leading to an overconfident belief in replicability. We demonstrate the adverse consequences of this statistical significance filter by conducting six direct replication attempts (168 participants in total) of published results from a recent paper. We show that the published claims are so noisy that even non-significant results are fully compatible with them. We also demonstrate the contrast between such small-sample studies and a larger-sample study (100 participants); the latter generally yields less noisy estimates but also a smaller effect size, which looks less compelling but is more realistic. We make several suggestions for improving best practices in psycholinguistics and related areas.]]></description>
<dc:subject>statistics</dc:subject>
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<item rdf:about="https://speak-statistics-to-power.github.io/fairness/">
    <title>Fairness</title>
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    <link>https://speak-statistics-to-power.github.io/fairness/</link>
    <dc:creator>amy</dc:creator><dc:subject>machine_learning statistics discrimination</dc:subject>
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<item rdf:about="https://www.fatconference.org/2018/index.html">
    <title>FAT* - 2018 Home</title>
    <dc:date>2017-12-19T17:44:53+00:00</dc:date>
    <link>https://www.fatconference.org/2018/index.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[The FAT* Conference 2018 is a two-day event that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. This inaugural conference builds on success of prior workshops like FAT/ML, FAT/Rec, DAT, Ethics in NLP, and others.

The inaugural 2018 FAT* Conference will be held February 23 and 24th, 2018 at New York University, NYC.]]></description>
<dc:subject>machine_learning statistics sociology ethics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://normaldeviate.wordpress.com/2012/06/12/statistics-versus-machine-learning-5-2/">
    <title>Statistics Versus Machine Learning « Normal Deviate</title>
    <dc:date>2017-10-09T17:14:44+00:00</dc:date>
    <link>https://normaldeviate.wordpress.com/2012/06/12/statistics-versus-machine-learning-5-2/</link>
    <dc:creator>amy</dc:creator><dc:subject>machine_learning statistics</dc:subject>
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<dc:identifier>https://pinboard.in/u:amy/b:e0d0977daf3f/</dc:identifier>
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<item rdf:about="https://stats.stackexchange.com/questions/6/the-two-cultures-statistics-vs-machine-learning">
    <title>The Two Cultures: statistics vs. machine learning? - Cross Validated</title>
    <dc:date>2017-10-09T17:12:13+00:00</dc:date>
    <link>https://stats.stackexchange.com/questions/6/the-two-cultures-statistics-vs-machine-learning</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics machine_learning</dc:subject>
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<item rdf:about="http://andrewgelman.com/2008/12/03/machine_learnin/">
    <title>Machine learning and statistics - Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2017-10-09T16:42:03+00:00</dc:date>
    <link>http://andrewgelman.com/2008/12/03/machine_learnin/</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics machine_learning</dc:subject>
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<item rdf:about="http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/">
    <title>Statistics vs. Machine Learning, fight! | AI and Social Science – Brendan O'Connor</title>
    <dc:date>2017-10-09T16:41:47+00:00</dc:date>
    <link>http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics machine_learning</dc:subject>
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<item rdf:about="http://statweb.stanford.edu/~tibs/stat315a/glossary.pdf">
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    <dc:date>2017-10-09T16:39:47+00:00</dc:date>
    <link>http://statweb.stanford.edu/~tibs/stat315a/glossary.pdf</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics machine_learning amusements</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://cloud.google.com/blog/big-data/2017/08/hyperparameter-tuning-in-cloud-machine-learning-engine-using-bayesian-optimization">
    <title>Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization | Google Cloud Big Data and Machine Learning Blog  |  Google Cloud Platform</title>
    <dc:date>2017-08-11T22:32:42+00:00</dc:date>
    <link>https://cloud.google.com/blog/big-data/2017/08/hyperparameter-tuning-in-cloud-machine-learning-engine-using-bayesian-optimization</link>
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<item rdf:about="https://soundcloud.com/nssd-podcast">
    <title>Not So Standard Deviations | Free Listening on SoundCloud</title>
    <dc:date>2017-06-29T23:55:45+00:00</dc:date>
    <link>https://soundcloud.com/nssd-podcast</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Not So Standard Deviations: The Data Science Podcast

Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

Co-hosts: Roger Peng of the Johns Hopkins Bloomberg School of Public Health and Hilary Parker of Stitch Fix.]]></description>
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<item rdf:about="http://callingbullshit.org/syllabus.html#Introduction">
    <title>Calling Bullshit — Syllabus</title>
    <dc:date>2017-05-26T15:21:34+00:00</dc:date>
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<item rdf:about="http://google.github.io/CausalImpact/">
    <title>CausalImpact</title>
    <dc:date>2014-09-12T18:11:31+00:00</dc:date>
    <link>http://google.github.io/CausalImpact/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[What does this package do?
The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package overcomes this difficulty using a structural Bayesian time-series model to estimate how the response metric would have evolved after the intervention if the intervention had not occurred.
]]></description>
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<item rdf:about="http://rpsychologist.com/how-to-work-with-google-ngram-data-sets-in-r-using-mysql/">
    <title>Seriously geeky, but handy: How to work with Google n-gram data sets in R using MySQL - http://t.co/i8hFIR8C</title>
    <dc:date>2012-04-14T07:53:15+00:00</dc:date>
    <link>http://rpsychologist.com/how-to-work-with-google-ngram-data-sets-in-r-using-mysql/</link>
    <dc:creator>amy</dc:creator><dc:subject>mysql statistics google</dc:subject>
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<item rdf:about="http://www.thennt.com/">
    <title>The NNT | Quick Summaries of Evidence-Based Medicine</title>
    <dc:date>2012-03-15T22:22:56+00:00</dc:date>
    <link>http://www.thennt.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>medicine statistics</dc:subject>
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<item rdf:about="http://jjiaa.com/completethis.html">
    <title>Complete This</title>
    <dc:date>2011-10-04T06:51:58+00:00</dc:date>
    <link>http://jjiaa.com/completethis.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[based on google suggest data]]></description>
<dc:subject>search statistics google</dc:subject>
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</item>
<item rdf:about="http://m.guardian.co.uk/commentisfree/2011/sep/16/bad-science-dodgy-stats?cat=commentisfree&amp;type=article">
    <title>The special trick that helps identify dodgy stats</title>
    <dc:date>2011-09-17T22:12:46+00:00</dc:date>
    <link>http://m.guardian.co.uk/commentisfree/2011/sep/16/bad-science-dodgy-stats?cat=commentisfree&amp;type=article</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Benford's law]]></description>
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<item rdf:about="http://news.bbc.co.uk/2/hi/uk_news/magazine/8153539.stm">
    <title>BBC NEWS | UK | Magazine | A scanner to detect terrorists</title>
    <dc:date>2011-07-26T13:19:08+00:00</dc:date>
    <link>http://news.bbc.co.uk/2/hi/uk_news/magazine/8153539.stm</link>
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<item rdf:about="http://laughingmeme.org/2011/07/23/cost-of-false-positives/">
    <title>Cost of false positives - Laughing Meme</title>
    <dc:date>2011-07-26T13:18:43+00:00</dc:date>
    <link>http://laughingmeme.org/2011/07/23/cost-of-false-positives/</link>
    <dc:creator>amy</dc:creator><dc:subject>community statistics</dc:subject>
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<item rdf:about="http://www.google.com/publicdata/home">
    <title>Google Public Data Explorer</title>
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    <link>http://www.google.com/publicdata/home</link>
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<item rdf:about="http://code.google.com/appengine/articles/mr/mapper.html">
    <title>Modern Funnel Analytics Using the Mapper API - Google App Engine - Google Code</title>
    <dc:date>2011-04-26T23:11:31+00:00</dc:date>
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<item rdf:about="http://blog.okcupid.com/index.php/the-best-questions-for-first-dates/">
    <title>The Best Questions For A First Date « OkTrends</title>
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    <title>How Etsy is Using Node.js</title>
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<item rdf:about="http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/">
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    <dc:creator>amy</dc:creator><description><![CDATA[A Tutorial on Clustering Algorithms
]]></description>
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    <title>PLoS Medicine: Why Most Published Research Findings Are False</title>
    <dc:date>2010-11-29T15:21:54+00:00</dc:date>
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<item rdf:about="http://www.dataists.com/2010/09/a-taxonomy-of-data-science/">
    <title>dataists » Blog Archive » A Taxonomy of Data Science</title>
    <dc:date>2010-09-25T18:17:27+00:00</dc:date>
    <link>http://www.dataists.com/2010/09/a-taxonomy-of-data-science/</link>
    <dc:creator>amy</dc:creator><dc:subject>datamining analytics machine_learning statistics analysis</dc:subject>
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<dc:identifier>https://pinboard.in/u:amy/b:c9af3dd7779c/</dc:identifier>
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<item rdf:about="http://www.wired.com/dangerroom/2010/08/open-source-wikileaked-docs-illustrated-afghan-meltdown/">
    <title>Open Source Tools Turn WikiLeaks Into Illustrated Afghan Meltdown (Updated) | Danger Room | Wired.com</title>
    <dc:date>2010-08-10T15:18:56+00:00</dc:date>
    <link>http://www.wired.com/dangerroom/2010/08/open-source-wikileaked-docs-illustrated-afghan-meltdown/</link>
    <dc:creator>amy</dc:creator><dc:subject>history usa war statistics visualizations</dc:subject>
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<item rdf:about="http://blog.revolutionanalytics.com/2010/07/a-free-book-on-probability-and-statistics-with-r.html">
    <title>Revolutions: A free book on probability and statistics with R</title>
    <dc:date>2010-07-30T05:55:57+00:00</dc:date>
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    <dc:creator>amy</dc:creator><description><![CDATA[A free book on probability and statistics with R -  #rstats /via @paulblaser]]></description>
<dc:subject>#rstats rstats book statistics</dc:subject>
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<item rdf:about="http://www.flickr.com/photos/broken_simulacra/83645099/">
    <title>Demographics on Flickr - Photo Sharing!</title>
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    <dc:creator>amy</dc:creator><description><![CDATA[Brien Lane. Actual demographics taken from the 1996 Australian census. The whole alleyway has been painted with statistical information - those lines are bar charts. ]]></description>
<dc:subject>melbourne statistics demographics art</dc:subject>
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<item rdf:about="http://code.google.com/apis/predict/">
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    <dc:creator>amy</dc:creator><description><![CDATA[The Prediction API enables access to Google's machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google's infrastructure and scale effortlessly as your data grows in size and complexity.]]></description>
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<item rdf:about="http://mydatamine.com/?p=308">
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<item rdf:about="http://blog.okcupid.com/index.php/2010/02/16/the-case-for-an-older-woman/">
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    <dc:creator>amy</dc:creator><description><![CDATA[How to split up the US: what facebook tells us about the shape of the country.  More:  (via @brady)]]></description>
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<item rdf:about="http://www.fivethirtyeight.com/2009/12/odds-of-airborne-terror.html">
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    <dc:creator>amy</dc:creator><description><![CDATA[...It's time to fight back.

The solution we're creating is simple: an open-source filter software that can detect rampant stupidity in written English. This will be accomplished with weighted Bayesian or similar analysis and some rules-based processing, similar to spam detection engines. The primary challenge inherent in our task is that stupidity is not a binary distinction, but rather a matter of degree. To this end, we're collecting a ranked corpus of stupid text, gleaned from user comments on public websites and ranked on a five-point scale.]]></description>
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<item rdf:about="http://www.cerebralmastication.com/?p=8">
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<item rdf:about="http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?_r=2&amp;pagewanted=all">
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    <dc:creator>amy</dc:creator><dc:subject>statistics analysis programming computer_languages datamining</dc:subject>
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<item rdf:about="http://www.meetup.com/Austin-R-Users-Group/">
    <title>Austin Area R Users Group (Austin, TX) - Meetup.com</title>
    <dc:date>2009-10-29T14:21:44+00:00</dc:date>
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    <dc:creator>amy</dc:creator><dc:subject>austin statistics analysis</dc:subject>
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    <title>R, the Software, Finds Fans in Data Analysts - NYTimes.com</title>
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<item rdf:about="https://graphics.stanford.edu/wikis/cs448b-09-fall/">
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<item rdf:about="http://robjhyndman.com/researchtips/workflow-in-r/">
    <title>Workflow in R | Research tips</title>
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    <dc:creator>amy</dc:creator><dc:subject>statistics tips</dc:subject>
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<item rdf:about="http://heather.cs.ucdavis.edu/~matloff/r.html">
    <title>R Tutorial</title>
    <dc:date>2009-10-28T17:41:42+00:00</dc:date>
    <link>http://heather.cs.ucdavis.edu/~matloff/r.html</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics tutorials</dc:subject>
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<item rdf:about="http://oreilly.com/catalog/9780596804787">
    <title>Data Mashups in R - O'Reilly Media</title>
    <dc:date>2009-10-28T17:40:45+00:00</dc:date>
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    <dc:creator>amy</dc:creator><dc:subject>statistics computer_languages books reference</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:2ff054c5f9c9/</dc:identifier>
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<item rdf:about="http://www.redmonk.com/jgovernor/2009/10/22/critical-mass-bringing-physics-to-our-social-network-pablum/">
    <title>James Governor</title>
    <dc:date>2009-10-22T21:14:03+00:00</dc:date>
    <link>http://www.redmonk.com/jgovernor/2009/10/22/critical-mass-bringing-physics-to-our-social-network-pablum/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[New post by @monkchips "blew my head clean off". How many so-called “soc media experts” have read any network theory?" http://bit.ly/2hfWSc]]></description>
<dc:subject>twitter_fav @KathySierra books physics statistics social_media</dc:subject>
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<item rdf:about="http://www-stat.stanford.edu/~tibs/ElemStatLearn/">
    <title>Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.</title>
    <dc:date>2009-10-16T17:39:45+00:00</dc:date>
    <link>http://www-stat.stanford.edu/~tibs/ElemStatLearn/</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics datamining machine_learning reference books academia</dc:subject>
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<item rdf:about="http://www-stat.stanford.edu/~tibs/ElemStatLearn//">
    <title>Elements of Statistical Learning: data mining, inference, and prediction.
2nd Edition.</title>
    <dc:date>2009-10-16T15:54:10+00:00</dc:date>
    <link>http://www-stat.stanford.edu/~tibs/ElemStatLearn//</link>
    <dc:creator>amy</dc:creator><description><![CDATA[pdf available online]]></description>
<dc:subject>statistics datamining academia reference machine_learning</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:accf865c2a4e/</dc:identifier>
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<item rdf:about="http://manyeyes.alphaworks.ibm.com/manyeyes/">
    <title>Many Eyes</title>
    <dc:date>2009-09-23T19:12:20+00:00</dc:date>
    <link>http://manyeyes.alphaworks.ibm.com/manyeyes/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Just realized that my old friend and information visualization wizard @wattenberg is on Twitter. Check this out: http://bit.ly/2MdMUH]]></description>
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<dc:identifier>https://pinboard.in/u:amy/b:fdc6ccd7eda5/</dc:identifier>
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<item rdf:about="http://www.scientific-computing.com/features/feature.php?feature_id=245">
    <title>-  - Scientific Computing World</title>
    <dc:date>2009-09-06T20:24:36+00:00</dc:date>
    <link>http://www.scientific-computing.com/features/feature.php?feature_id=245</link>
    <dc:creator>amy</dc:creator><description><![CDATA["Analysis is the mother of invention" - good article on models of investion and the role of statistical analysis http://bit.ly/4mVIpY]]></description>
<dc:subject>statistics analysis innovation twitter_fav @Werner</dc:subject>
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<item rdf:about="http://www.tgdaily.com/content/view/43494/140/">
    <title>TG Daily - Computers unlock 4,000 year old language</title>
    <dc:date>2009-08-04T14:06:33+00:00</dc:date>
    <link>http://www.tgdaily.com/content/view/43494/140/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[It is possible to see the underlying grammatical structure of the Indus script even if the scientists don't know what each word means. Such a model is stage one for decipherment, because any meaning ascribed to a symbol must make sense in the context of other symbols that precede or follow it.
]]></description>
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<item rdf:about="http://opendotdotdot.blogspot.com/2009/07/open-source-cognitive-science.html">
    <title>open...: Open Source Cognitive Science</title>
    <dc:date>2009-07-31T14:54:10+00:00</dc:date>
    <link>http://opendotdotdot.blogspot.com/2009/07/open-source-cognitive-science.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[R stats package and Sweave]]></description>
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<item rdf:about="http://www.fivethirtyeight.com/2009/07/challenge-to-climate-change-skeptics.html">
    <title>FiveThirtyEight: Politics Done Right: A Challenge to Climate Change Skeptics</title>
    <dc:date>2009-07-19T23:09:08+00:00</dc:date>
    <link>http://www.fivethirtyeight.com/2009/07/challenge-to-climate-change-skeptics.html</link>
    <dc:creator>amy</dc:creator><dc:subject>climate statistics</dc:subject>
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    <title>Federal IT Dashboard</title>
    <dc:date>2009-07-02T19:35:16+00:00</dc:date>
    <link>http://it.usaspending.gov/</link>
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<item rdf:about="http://www.autonlab.org/tutorials/">
    <title>Statistical Data Mining Tutorials</title>
    <dc:date>2009-06-05T20:32:36+00:00</dc:date>
    <link>http://www.autonlab.org/tutorials/</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics datamining machine_learning tutorials reference resources</dc:subject>
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<item rdf:about="http://www.withouthotair.com/download.html">
    <title>David MacKay: Sustainable Energy - without the hot air: Download</title>
    <dc:date>2009-04-09T13:37:11+00:00</dc:date>
    <link>http://www.withouthotair.com/download.html</link>
    <dc:creator>amy</dc:creator><dc:subject>environment books climate statistics physics</dc:subject>
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<item rdf:about="http://www.boingboing.net/2009/04/09/sustainable-energy-w.html">
    <title>Sustainable Energy Without the Hot Air: the Freakonomics of conservation, climate and energy - Boing Boing</title>
    <dc:date>2009-04-09T13:36:02+00:00</dc:date>
    <link>http://www.boingboing.net/2009/04/09/sustainable-energy-w.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[This is to energy and climate what Freakonomics is to economics: an accessible, meaty, by-the-numbers look at the physics and practicalities of energy. MacKay, a Cambridge Physics prof, approaches the subject of carbon and sustainability with a scientific, numeric eye.]]></description>
<dc:subject>books environment statistics physics climate</dc:subject>
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<item rdf:about="http://harpers.org/index/">
    <title>Search Harper's Index</title>
    <dc:date>2009-02-15T16:14:19+00:00</dc:date>
    <link>http://harpers.org/index/</link>
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<item rdf:about="http://www.ohloh.net/languages/compare?measure=contributors&amp;percent=&amp;l0=perl&amp;l1=python&amp;l2=ruby&amp;l3=java&amp;l4=-1&amp;commit=Update">
    <title>Compare Languages - Ohloh</title>
    <dc:date>2009-01-20T20:04:20+00:00</dc:date>
    <link>http://www.ohloh.net/languages/compare?measure=contributors&amp;percent=&amp;l0=perl&amp;l1=python&amp;l2=ruby&amp;l3=java&amp;l4=-1&amp;commit=Update</link>
    <dc:creator>amy</dc:creator><dc:subject>computer_languages development statistics visualizations</dc:subject>
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<item rdf:about="http://cli.gs/">
    <title>Short URLs with analytics, social media monitoring, and geotargeting - Cligs</title>
    <dc:date>2009-01-10T15:27:30+00:00</dc:date>
    <link>http://cli.gs/</link>
    <dc:creator>amy</dc:creator><dc:subject>statistics internet analysis marketing twitter tools web-analytics</dc:subject>
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