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Lognormal-de Wijsian Geostatistics for Ore Evaluation
Krige on kriging. I have to admit I hadn't fully realized that the historical context was "keep South Africa going"...
in_NB  have_read  spatial_statistics  prediction  statistics  geology  to_teach:data_over_space_and_time 
26 days ago by cshalizi
[math/0506080] Two new Markov order estimators
"We present two new methods for estimating the order (memory depth) of a finite alphabet Markov chain from observation of a sample path. One method is based on entropy estimation via recurrence times of patterns, and the other relies on a comparison of empirical conditional probabilities. The key to both methods is a qualitative change that occurs when a parameter (a candidate for the order) passes the true order. We also present extensions to order estimation for Markov random fields."
in_NB  markov_models  statistical_inference_for_stochastic_processes  model_selection  recurrence_times  entropy_estimation  information_theory  stochastic_processes  have_read  have_talked_about  random_fields 
26 days ago by cshalizi
A personal essay on Bayes factors
I would have said nobody blogs like this anymore, and I am very happy to be very wrong.
have_read  model_selection  bayesianism  statistics  psychology  social_science_methodology  via:tslumley 
4 weeks ago by cshalizi
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
"Randomized neural networks are immortalized in this AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. What are you doing?'' asked Minsky. I am training a randomly wired neural net to play tic-tac-toe,'' Sussman replied. Why is the net wired randomly?'' asked Minsky. Sussman replied, I do not want it to have any preconceptions of how to play.'' Minsky then shut his eyes. Why do you close your eyes?'' Sussman asked his teacher. So that the room will be empty,'' replied Minsky. At that moment, Sussman was enlightened. We analyze shallow random networks with the help of concentration of measure inequalities. Specifically, we consider architectures that compute a weighted sum of their inputs after passing them through a bank of arbitrary randomized nonlinearities. We identify conditions under which these networks exhibit good classification performance, and bound their test error in terms of the size of the dataset and the number of random nonlinearities."

--- Have I never bookmarked this before?
in_NB  approximation  kernel_methods  random_projections  statistics  prediction  classifiers  rahimi.ali  recht.benjamin  machine_learning  have_read 
5 weeks ago by cshalizi
[1205.4591] Forecastable Component Analysis (ForeCA)
" introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converging algorithm with a fast eigenvector solution. Applications to financial and macro-economic time series show that ForeCA can successfully discover informative structure, which can be used for forecasting as well as classification. The R package ForeCA (this http URL) accompanies this work and is publicly available on CRAN."
to:NB  have_read  time_series  kith_and_kin  goerg.georg  prediction  statistics  to_teach:data_over_space_and_time 
5 weeks ago by cshalizi
Safe spaces, academic freedom, and the university as a complex association - Bleeding Heart Libertarians
This is great, but I am less convinced than Levy is that (at least some of) the demands aren't for making the _whole_ university into safe spaces for sub-associations.
academia  academic_freedom  freedom_of_expression  levy.jacob_t.  have_read  via:? 
5 weeks ago by cshalizi
Analysis of a complex of statistical variables into principal components.
"The problem is stated in detail, a method of analysis is derived and its geometrical meaning shown, methods of solution are illustrated and certain derivative problems are discussed. (To be concluded in October issue.) "

--- In which Harold Hotelling re-invents principal components analysis, 32 years after Karl Pearson. (Part 2: http://dx.doi.org/10.1037/h0070888)
to:NB  have_read  principal_components  data_analysis  hotelling.harold  re:ADAfaEPoV 
5 weeks ago by cshalizi
On lines and planes of closest fit to systems of points in space (K. Pearson, 1901)
In which Karl Pearson invents principal components analysis, with the entirely sensible objective of finding low-dimensional approximations to high-dimensional data. (i.e., basically the way I teach it!)
to:NB  principal_components  data_analysis  pearson.karl  re:ADAfaEPoV  have_read 
5 weeks ago by cshalizi
Parzen : On Estimation of a Probability Density Function and Mode
In which Parzen introduces kernel density estimation, three years after Rosenblatt introduced it _in the same journal_.
to:NB  statistics  density_estimation  have_read  parzen.emanuel  re:ADAfaEPoV 
5 weeks ago by cshalizi
Rosenblatt : Remarks on Some Nonparametric Estimates of a Density Function (1956)
"This note discusses some aspects of the estimation of the density function of a univariate probability distribution. All estimates of the density function satisfying relatively mild conditions are shown to be biased. The asymptotic mean square error of a particular class of estimates is evaluated."

--- In which Rosenblatt introduces kernel density estimation.
to:NB  statistics  density_estimation  have_read  rosenblatt.murray  re:ADAfaEPoV 
5 weeks ago by cshalizi
cultural cognition project - Cultural Cognition Blog - Return of the chick sexers . . .
"To put it in terms used to appraise scientific methods, we know the professional judgment of the chick sexer is not only reliable—consistently attuned to whatever it is that appropriately trained members of their craft are unconsciously discerning—but also valid: that is, we know that the thing the chick sexers are seeing (or measuring, if we want to think of them as measuring instruments of a special kind) is the thing we want to ascertain (or measure), viz., the gender of the chicks.
"In the production of lawyers, we have reliability only, without validity—or at least without validation.  We do successfully (remarkably!) train lawyers to make out the same patterns when they focus their gaze at the “mystifying cloud of words” that Cardozo identified the law as comprising. But we do nothing to assure that what they are discerning is the form of justice that the law is held forth as embodying.
"Observers fret—and scholars using empirical methods of questionable reliability and validity purport to demonstrate—that judges are mere “politicians in robes,” whose decisions reflect the happenstance of their partisan predilections.
"That anxiety that judges will disagree based on their “ideologies” bothers me not a bit.
"What does bother me—more than just a bit—is the prospect that the men and women I’m training to be lawyers and judges will, despite the diversity of their political and moral sensibilities, converge on outcomes that defy the basic liberal principles that we expect to animate our institutions.
"The only thing that I can hope will stop that from happening is for me to tell them that this is how it works.  Because if it troubles me, I have every reason to think that they, as reflective decent people committed to respecting the freedom & reason of others, will find some of this troubling too.
"Not so troubling that they can’t become good lawyers. 
"But maybe troubling enough that they won't stop being reflective moral people in their careers as lawyers; troubling enough so that if they find themselves in a position to do so, they will enrich the stock of virtuous-lawyer prototypes that populate our situation sense  by doing something  that they, as reflective, moral people—“conservative” or “liberal”—recognize is essential to reconciling being a “good lawyer” with being a member of a profession essential to the good of a liberal democratic regime."

--- Preach, preach! (But this is also one turn away from seeing the legal sensibility as itself ideological, in the service of particular social interests...)
have_read  cognition  expertise  cultural_transmission_of_cognitive_tools  tacit_knowledge  professions  ideology  moral_responsibility  kahan.dan  via:tsuomela 
6 weeks ago by cshalizi
If we already understood the brain, would we even know it? – [citation needed]
"(it turns out that if you measure people’s physical height under an array of different conditions, the measurements are all strongly correlated–yet strangely, we don’t see scientists falling over themselves to try to find the causal factor that explains why some people are taller than others)."

That's a great analogy. This part is great too:

Lest I be accused of some kind of neuroscientific nihilism, let me be clear: I’m not saying that there are no new facts left to learn about the dynamics of the DMN. Quite the contrary. It’s clear there’s a ton of stuff we don’t know about the various brain regions and circuits that comprise the thing we currently refer to as the DMN. It’s just that that stuff lies almost entirely at levels of analysis below the level at which the DMN emerges as a coherent system. At the level of cognitive neuroimaging, I would argue that we actually already have a pretty darn good idea about what the functional correlates of DMN regions are–and for that matter, I think we also already pretty much “understand” what all of the constituent regions within the DMN do individually. So if we want to study the DMN productively, we may need to give up on high-level questions like “what are the cognitive functions of the DMN?”, and instead satisfy ourselves with much narrower questions that focus on only a small part of the brain dynamics that, when measured and analyzed in a certain way, get labeled “default mode network”.

And it applies to Econ (macro vs micro etc) too.
knowledge  explanation  complexity  have_read  via:cshalizi  ***  Neuroscience 
7 weeks ago by MarcK
[1808.00023] The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
"The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area."

--- ETA: This is a really good and convincing paper.
in_NB  prediction  algorithmic_fairness  goel.sharad  via:rvenkat  have_read  heard_the_talk 
8 weeks ago by cshalizi
If we already understood the brain, would we even know it? – [citation needed]
"What I’m suggesting is that, when we say things like “we don’t really understand the brain yet”, we’re not really expressing factual statements about the collective sum of neuroscience knowledge currently held by all human beings. What each of us really means is something more like there are questions I personally am able to pose about the brain that seem to make sense in my head, but that I don’t currently know the answer to–and I don’t think I could piece together the answer even if you handed me a library of books containing all of the knowledge we’ve accumulated about the brain."
have_read  complexity  emergence  explanation  neuroscience  yarkoni.tal 
8 weeks ago by cshalizi

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