Difference between revisions of "2014 Concepts Study Page"

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what is computational statistics?<br>
what is computational statistics?<br>
calculus of interence<br>
calculus of inference<br>
probability axioms<br>
probability axioms<br>
Law of Or-ing, Law of And-ing, Law of Exhaustion<br>
Law of Or-ing, Law of And-ing, Law of Exhaustion<br>

Latest revision as of 14:05, 23 April 2014

"Tell me about..."

Segment 1: Let's talk about probability

what is computational statistics?
calculus of inference
probability axioms
Law of Or-ing, Law of And-ing, Law of Exhaustion
Law of De-Anding (Law of Total Probability)

Segment 2: Bayes

Bayes Theorem
EME hypotheses
contrast Bayesians and Frequentists
probabilities modified by data
prior probability
posterior probability
evidence factor
Bayes denominator
background information
commutativity and associativity of evidence
Hempel's paradox

Segment 3: Monty Hall

the Monty Hall problem

Segment 4: The Jailer's Tip

uninteresting parameters in a model
probability density function
Dirac delta function
massed prior
uniform prior
uninformative prior

Segment 5: Bernoulli Trials

Bernoulli trials
sufficient statistic
conjugate prior
beta distribution

Segment 6: The Towne Family Tree

variable length short tandem repeat (VLSTR)
binomial distribution
conditional independence
naive Bayes models
improper prior
log-uniform prior
paradigm for Bayesian parameter estimation
statistical model
data trimming

Segment 7: Central Tendency and Moments

measures of central tendency
mean minimizes mean square deviation
median minimizes mean absolute deviation
centered moments
skewness and kurtosis
standard deviation
additivity of mean and variance
semi-invariants of Gaussian and Poisson

Segment 8: Some Standard Distributions

normal (Gaussian) distribution
Student distribution
Cauchy distribution
heavy-tailed distributions
William Sealy Gosset
exponential distribution
lognormal distribution
gamma distribution
chi-square distribution
probability density function (PDF)
cumulative distribution function (CDF)

Segment 9: Characteristic Functions

characteristic function of a distribution
Fourier convolution theorem
characteristic function of a Gaussian
characteristic function of Cauchy distribution

Segment 10: The Central Limit Theorem

central limit theorem (CLT)
Taylor series around zero can fail
maximum a posteriori (MAP)
maximum likelihood (MLE)
sample mean and variance
estimate parameters of a Gaussian

Segment 11: Random Deviates

random deviate
transformation method (random deviates)
rejection method (random deviates)
ratio of uniforms method (random deviates)
squeeze (random deviates)
Leva's algorithm for normal deviates

Segment 12: P-Value Tests

p-value test
null hypothesis
test statistic
advantage of tail tests over Bayesian methods
distribution of p-values under the null hypothesis
p-test critical region
one-sided vs. two-sided p-value tests

Segment 13: The Yeast Genome

Saccharomyces cerevisiae
multinomial distribution

Segment 14: Bayesian Criticism of P-Values

stopping rule paradox
Bayes odds ratio
Normal approximation to binomial distribution
Ronald Aylmer Fisher
p=0.05 pros and cons

Segment 15: The Towne Family -- Again

posterior predictive p-value
empirical Bayes

Segment 16: Multiple Hypotheses

multiple hypothesis correction
Bonferroni correction
false discovery rate (FDR)
Bayesian approach to multiple hypotheses

Segment 17: The Multivariate Normal Distribution

multivariate normal distribution
covariance matrix
estimate mean, covariance from multivariate data
fitting data by a multivariate normal distribution
slice or projection of a multivariate normal r.v.
Cholesky decomposition
how to generate multivariate normal deviates
how to compute and draw error ellipses

Segment 18: The Correlation Matrix

covariance matrix
linear correlation matrix
test for correlation

Segment 19: The Chi-Square Statistic

chi-square statistic
chi-square distribution
transformation law of probabilities
characteristic function of chi-square distribution
generalization of chi-square to non-independent data

Segment 20: Nonlinear Least Squares Fitting

Normal error model
correlated Normal error model
maximum likelihood estimation of parameters
relation of chi-square to posterior probability
nonlinear least squares fitting
chi-square fitting
accuracy of fitted parameters
Hessian matrix and relation to covariance matrix
posterior distribution of fitted parameters
calculation of Hessian matrix

Segment 21: Marginalize vs. Condition Uninteresting Fitted Parameters

how to marginalize over uninteresting parameters
how to condition on known parameter values
covariance matrix of fitted parameters vs. of data
consistency (property of MLE)
asymptotic efficiency (property of MLE)
Fisher Information Matrix
asymptotic normality (property of MLE)
how to get confidence intervals from chi-square values

Segment 22: Uncertainty of Derived Parameters

linearized propagation of errors
sampling the posterior distribution (in least squares fitting)
ratio of two normals as example of something

Segment 23: Bootstrap Estimation of Uncertainty

bootstrap resampling
population distribution vs. sample distribution
drawing with and without replacement
bootstrap theorem
compare bootstrap with sampling the posterior

Segment 24: Goodness of Fit

precision improves as square root of data quantity
what chi-square value indicates a good fit?
degrees of freedom in chi-square fit
goodness-of-fit p-value (in least squares fitting)
number of degrees of freedom
linear constraints (chi-square)
nonlinear constraints (chi-square)

Segment 27: Mixture Models

forward statistical model
mixture model
assignment vector (mixture model)
marginalization in mixture models
hierarchical Bayesian models

Segment 28: Gaussian Mixture Models in 1-D

Gaussian mixture model
expectation-maximization (EM) methods
probabilistic assignment to components (GMMs)
Expectation or E-step
Maximization or M-step
overall likelihood of a GMM
log-sum-exp formula

Segment 29: GMMs in N-Dimensions

starting values for GMM iteration
number of components in a GMM (pros and cons)
K-means clustering

Segment 30: Expectation Maximization (EM) Methods

Jensen's inequality
concave function (EM methods)
EM theorem (e.g., geometrical interpretation)
missing data (EM methods)
GMM as an EM: what is the missing data, what are the parameters?

Segment 31: A Tale of Model Selection

use of Student distributions vs. normal distribution
heavy-tailed models in MLE
model selection
Akaiki information criterion (AIC)
Bayes information criterion (BIC)

Segment 32: Contingency Tables, A First Look

contingency table
row or column marginals
chi-square or Pearson statistic for contingency table
conditions vs. factors
hypergeometric distribution
multinomial distribution

Segment 33: Contingency Table Protocols and Fisher Exact Test

retrospective analysis or case/control study
prospective experiment or longitudinal study
nuisance parameter
cross-sectional or snapshot study
example of protocol with all marginals fixed
Fisher's Exact Test
sufficient statistic (re contingency tables)
Wald statistic (re contingency tables)

Segment 34: PermutationTests

Permutation Test (re contingency tables)
Monte Carlo calculation

Segment 37: A Few Bits of Information Theory

probable vs. improbable sequences (re entropy)
Shannon's definition of entropy
bits vs. nats
maximally compressed message (re entropy)

Segment 38: Mutual Information

monographic vs. digraphic entropy
conditional entropy
mutual information
side information
Kelly's formula for proportional betting
Kullback-Leibler distance
KL-distance as competitive edge in betting

Segment 39: MCMC and Gibbs Sampling

Bayes denominator (re MCMC)
sampling the posterior distribution (re MCMC)
Markov chain
detailed balance
ergodic sequence
Metropolis-Hastings algorithm
proposal distribution (re MCMC)
Gibbs sampler

Segments 40 and 41: MCMC Examples

waiting time in a Poisson process
good vs. bad proposal generators in MCMC

Segment 47: Low Rank Approximation of Data

data matrix or design matrix
singular value decomposition (SVD)
orthogonal matrix
optimal decomposition into rank 1 matrices
singular values

Segment 48: Principal Component Analysis

principal component analysis (PCA)
diagonalizing the covariance matrix
how much total variance is explained by principal components?
dimensional reduction