nhaliday + frequentist   11

trees are harlequins, words are harlequins — bayes: a kinda-sorta masterpost
lol, gwern: https://www.reddit.com/r/slatestarcodex/comments/6ghsxf/biweekly_rational_feed/diqr0rq/
> What sort of person thinks “oh yeah, my beliefs about these coefficients correspond to a Gaussian with variance 2.5″? And what if I do cross-validation, like I always do, and find that variance 200 works better for the problem? Was the other person wrong? But how could they have known?
> ...Even ignoring the mode vs. mean issue, I have never met anyone who could tell whether their beliefs were normally distributed vs. Laplace distributed. Have you?
I must have spent too much time in Bayesland because both those strike me as very easy and I often think them! My beliefs usually are Laplace distributed when it comes to things like genetics (it makes me very sad to see GWASes with flat priors), and my Gaussian coefficients are actually a variance of 0.70 (assuming standardized variables w.l.o.g.) as is consistent with field-wide meta-analyses indicating that d>1 is pretty rare.
ratty  ssc  core-rats  tumblr  social  explanation  init  philosophy  bayesian  thinking  probability  stats  frequentist  big-yud  lesswrong  synchrony  similarity  critique  intricacy  shalizi  scitariat  selection  mutation  evolution  priors-posteriors  regularization  bias-variance  gwern  reddit  commentary  GWAS  genetics  regression  spock  nitty-gritty  generalization  epistemic  🤖  rationality  poast  multi  best-practices  methodology  data-science 
august 2017 by nhaliday
Stat 260/CS 294: Bayesian Modeling and Inference
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
unit  course  berkeley  expert  michael-jordan  machine-learning  acm  bayesian  probability  stats  lecture-notes  priors-posteriors  markov  monte-carlo  frequentist  latent-variables  decision-theory  expert-experience  confidence  sampling 
july 2017 by nhaliday
probability - Why does a 95% Confidence Interval (CI) not imply a 95% chance of containing the mean? - Cross Validated
The confidence interval is the answer to the request: "Give me an interval that will bracket the true value of the parameter in 100p% of the instances of an experiment that is repeated a large number of times." The credible interval is an answer to the request: "Give me an interval that brackets the true value with probability pp given the particular sample I've actually observed." To be able to answer the latter request, we must first adopt either (a) a new concept of the data generating process or (b) a different concept of the definition of probability itself.


PS. Note that my question is not about the ban itself; it is about the suggested approach. I am not asking about frequentist vs. Bayesian inference either. The Editorial is pretty negative about Bayesian methods too; so it is essentially about using statistics vs. not using statistics at all.


q-n-a  overflow  nibble  stats  data-science  science  methodology  concept  confidence  conceptual-vocab  confusion  explanation  thinking  hypothesis-testing  jargon  multi  meta:science  best-practices  error  discussion  bayesian  frequentist  hmm  publishing  intricacy  wut  comparison  motivation  clarity  examples  robust  metabuch  🔬  info-dynamics  reference 
february 2017 by nhaliday
A Fervent Defense of Frequentist Statistics - Less Wrong
Short summary. This essay makes many points, each of which I think is worth reading, but if you are only going to understand one point I think it should be “Myth 5″ below, which describes the online learning framework as a response to the claim that frequentist methods need to make strong modeling assumptions. Among other things, online learning allows me to perform the following remarkable feat: if I’m betting on horses, and I get to place bets after watching other people bet but before seeing which horse wins the race, then I can guarantee that after a relatively small number of races, I will do almost as well overall as the best other person, even if the number of other people is very large (say, 1 billion), and their performance is correlated in complicated ways.

If you’re only going to understand two points, then also read about the frequentist version of Solomonoff induction, which is described in “Myth 6″.


If you are like me from, say, two years ago, you are firmly convinced that Bayesian methods are superior and that you have knockdown arguments in favor of this. If this is the case, then I hope this essay will give you an experience that I myself found life-altering: the experience of having a way of thinking that seemed unquestionably true slowly dissolve into just one of many imperfect models of reality. This experience helped me gain more explicit appreciation for the skill of viewing the world from many different angles, and of distinguishing between a very successful paradigm and reality.

If you are not like me, then you may have had the experience of bringing up one of many reasonable objections to normative Bayesian epistemology, and having it shot down by one of many “standard” arguments that seem wrong but not for easy-to-articulate reasons. I hope to lend some reprieve to those of you in this camp, by providing a collection of “standard” replies to these standard arguments.
bayesian  philosophy  stats  rhetoric  advice  debate  critique  expert  lesswrong  commentary  discussion  regularizer  essay  exposition  🤖  aphorism  spock  synthesis  clever-rats  ratty  hi-order-bits  top-n  2014  acmtariat  big-picture  acm  iidness  online-learning  lens  clarity  unit  nibble  frequentist  s:**  expert-experience  subjective-objective 
september 2016 by nhaliday
Bayesianism, frequentism, and the planted clique, or do algorithms believe in unicorns? | Windows On Theory
But if you consider probabilities as encoding beliefs, then it’s quite likely that a computationally bounded observer is not certain whether {17} is in the clique or not. After all, finding a maximum clique is a hard computational problem. So if T is much smaller than the time it takes to solve the k-clique problem (which is n^{const\cdot k} as far as we know), then it might make sense for time T observers to assign a probability between 0 and 1 to this event. Can we come up with a coherent theory of such probabilities?
research  tcs  complexity  probability  stats  algorithms  yoga  speculation  tcstariat  frontier  insight  exposition  rand-approx  synthesis  big-picture  boaz-barak  org:bleg  nibble  frequentist  bayesian  subjective-objective 
april 2016 by nhaliday

bundles : academeacmsci

related tags

accretion  acm  acmtariat  adversarial  advice  algorithms  aphorism  apollonian-dionysian  bayesian  berkeley  best-practices  bias-variance  big-picture  big-yud  bits  boaz-barak  bonferroni  books  clarity  clever-rats  commentary  comparison  complexity  concept  conceptual-vocab  confidence  confusion  core-rats  course  critique  data-science  debate  decision-theory  differential-privacy  discussion  encyclopedic  epistemic  error  essay  evolution  examples  expert  expert-experience  explanation  exposition  fisher  frequentist  frontier  gelman  generalization  genetics  google  gotchas  gradient-descent  GWAS  gwern  hi-order-bits  hmm  human-ml  hypothesis-testing  iidness  info-dynamics  information-theory  init  insight  intricacy  iteration-recursion  jargon  latent-variables  learning-theory  lecture-notes  lens  lesswrong  linearity  liner-notes  list  lower-bounds  machine-learning  markov  math  medicine  meta:science  metabuch  metameta  methodology  michael-jordan  ML-MAP-E  monte-carlo  motivation  mrtz  multi  mutation  nibble  nitty-gritty  no-go  online-learning  org:bleg  overflow  p:someday  p:whenever  perturbation  philosophy  poast  priors-posteriors  probability  publishing  q-n-a  quixotic  rand-approx  rationality  ratty  reading  recommendations  reddit  reference  reflection  regression  regularization  regularizer  research  research-program  rhetoric  robust  s:**  sampling  science  scitariat  selection  sensitivity  shalizi  similarity  social  speculation  spock  ssc  stats  subjective-objective  summary  synchrony  synthesis  tcs  tcstariat  thinking  top-n  tumblr  tutorial  unit  volo-avolo  wut  yoga  🔬  🤖 

Copy this bookmark: