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Index

ABC, 10.8
Anaconda, 0.3
Approximate Bayesian Computation, 10.8
Axtell, Robert, 3.4
abstract type, 2.7, 6.4
arrival rate, 8.5
Bayes factor, 5.3, 11.1, 11.2, 11.3, 12.4
Bayes’s theorem, 1.3
- derivation, 1.4
- odds form, 5.2
Bayesian framework, 2.3
Behavioral Risk Factor Surveillance System, 10.1
Bernoulli process, 7.2
Beta object, 4.5, 15.9
Boston, 8.1
Boston Bruins, 7.1
BRFSS, 10.1, 10.8
bacteria, 15.1
belly button, 15.1
beta distribution, 4.5, 15.2, 15.2
biased coin, 11.1
binomial coefficient, 15.6
binomial distribution, 12.3, 14.1, 14.2
binomial likelihood function, 4.5
biodiversity, 15.1
bogus, 10.3, 11.1
bucket, 13.5
bus stop problem, 7.8, 7.8
Campbell-Ricketts, Tom, 14.1
CDC, 10.1
Cdf, 3.6, 5.7, 6.5, 8.3, 15.10
Centers for Disease Control, 10.1
College Board, 12.2
Cromwell’s rule, 4.6
Cromwell, Oliver, 4.6
cache, 10.7, 13.4
calibration, 12.6
carcinoma, 13.3
causation, 14.1, 14.6
central credible interval, 9.7
classical estimation, 10.2
clone, 0.3
coefficient of variation, 10.1
coin toss, 1.1
collectively exhaustive, 1.5
complementary CDF, 15.12
concrete type, 2.7, 6.4
conditional distribution, 9.6, 9.8, 13.3, 13.6, 13.8, 15.11
conditional probability, 1.1
conjoint probability, 1.2
conjugate prior, 4.5
conjunction, 1.4
continuous distribution, 4.5
contributors, 0.5
convergence, 4.3, 4.6
cookie problem, 1.3, 2.2, 5.2
cookie.py, 2.2
correlated random value, 13.7
coverage, 15.12, 15.12
crank science, 10.1
credible interval, 3.5, 9.5
cumulative distribution function, 3.6, 8.3
cumulative probability, 13.7, 15.10
cumulative sum, 15.6
Davidson-Pilon, Cameron, 6.1
Dice problem, 3.1
Dirichlet distribution, 15.2, 15.10
Dungeons and Dragons, 3.1, 5.4
decision analysis, 6.1, 6.8, 6.9, 8.7
degree of belief, 1.1
density, 6.3, 6.4, 6.6, 10.2
dependence, 1.2, 9.6, 9.6
diachronic interpretation, 1.5
dice, 2.1, 3.1
dice problem, 3.2
distribution, 2.1, 5.7, 6.9
- operations, 5.4
divide-and-conquer, 1.8
doubling time, 13.1
ESP, 11.5
Euro problem, 4.1, 4.6, 10.8, 11.1
efficacy, 12.5
enumeration, 5.4, 5.5
error, 6.5
evidence, 1.4, 4.2, 5.3, 5.3, 9.5, 10.1, 11.1, 11.1, 11.2, 11.3, 11.4, 12.1
exception, 10.5
exponential distribution, 7.2, 7.6, 13.1
exponentiation, 5.5
extra-sensory perception, 11.5
fair coin, 11.1
fork, 0.3
forward problem, 14.1
Gaussian distribution, 6.3, 6.4, 6.4, 6.5, 7.1, 10.2, 10.8, 10.9, 12.3, 12.5, 12.6, 13.7
Gaussian PDF, 6.4
Gee, Steve, 6.2
Geiger counter problem, 14.1, 14.6
German tank problem, 3.2, 3.8
Git, 0.3
GitHub, 0.3
gamma distribution, 15.4, 15.6
generator, 13.7, 13.7, 15.10
growth rate, 13.7
Heuer, Andreas, 7.2
Hoag, Dirk, 7.7
Horsford, Eben Norton, 10.1
Hume, David, 11.5
heart attack, 1.1
height, 10.1
hierarchical model, 14.3, 14.6, 15.3
hockey, 7.1
horse racing, 5.1
hypothesis testing, 11.1
IQR, 10.9
implementation, 2.7, 6.4
independence, 1.2, 1.6, 5.4, 5.5, 9.6, 9.6, 12.9, 13.3, 15.2
informative prior, 3.8
insect sampling problem, 7.8
installation, 0.3
inter-quartile range, 10.9
interface, 2.7, 6.4
intuition, 1.7
inverse problem, 14.1
item response theory, 12.5
iterative modeling, 7.7
iterator, 13.4
Jaynes, E. T., 14.1
Joint, 9.5, 9.6, 9.7, 9.8, 10.2
Joint object, 15.11
Joint pmf, 9.2
joint distribution, 9.5, 9.8, 10.2, 12.9, 13.5, 13.6, 13.8, 15.2, 15.10, 15.11
KDE, 6.2, 6.4
Kidney tumor problem, 13.1
kernel density estimation, 6.2, 6.4
Likelihood, 2.3
least squares fit, 13.6

light bulb problem, 7.8
likelihood, 1.5, 6.5, 6.6, 6.6, 8.4, 9.3, 9.4, 10.2, 10.11, 11.2, 14.2
likelihood function, 3.2
likelihood ratio, 5.3, 11.2, 11.3, 12.4
linspace, 10.2
lions and tigers and bears, 15.2
locomotive problem, 3.2, 3.8, 10.8
log scale, 13.5
log transform, 10.5
log-likelihood, 10.6, 15.6, 15.6
logarithm, 10.5
M and M problem, 1.6, 2.6
MacKay, David, 4.1, 5.3, 8.9, 11.1
MakeMixture, 7.4, 7.6, 8.3, 8.6, 12.5, 15.9
Meckel, Johann, 10.1
Monty Hall problem, 1.7, 2.4
Mosteller, Frederick, 3.2
Mult, 2.2
marginal distribution, 9.5, 9.8, 15.2
matplotlib, 0.3
maximum, 5.5
maximum likelihood, 3.5, 4.2, 6.9, 9.7, 10.3, 10.5, 15.2
mean squared error, 3.2
median, 4.2
memoization, 10.7
meta-Pmf, 7.4, 7.6, 8.3, 8.6, 12.5, 15.9
meta-Suite, 14.3, 15.3
microbiome, 15.1
mixture, 5.6, 7.4, 7.4, 7.6, 8.3, 8.6, 8.6, 13.1, 15.9
modeling, 0.2, 3.8, 4.6, 7.7, 10.11, 12.1, 13.2, 13.3
modeling error, 12.5, 13.7, 13.7
multinomial coefficient, 15.3
multinomial distribution, 15.2, 15.3, 15.6
mutually exclusive, 1.5
National Hockey League, 7.1
NHL, 7.1
NumPy, 0.3
navel, 15.1
non-linear, 8.6
normal distribution, 6.4
normalize, 6.7
normalizing constant, 1.5, 1.6, 5.2, 14.4
nuisance parameter, 12.9
numpy, 6.4, 6.4, 6.5, 6.8, 7.1, 8.5, 10.2, 12.6, 15.2, 15.4, 15.5, 15.6, 15.6, 15.6, 15.7, 15.8, 15.8, 15.10
Olin College, 8.1
Oliver’s blood problem, 5.3
OTU, 15.9
objectivity, 3.8
observer bias, 8.2, 8.7
odds, 5.1
operational taxonomic unit, 15.9
optimization, 4.4, 10.7, 10.7, 14.4, 15.5
overtime, 7.5
Paintball problem, 9.1
PDF, 4.5, 7.1
Pdf, 6.3, 6.4
PEP 8, 0.4
Pmf, 5.7, 6.3
Pmf class, 2.1
Pmf methods, 2.1
Poisson distribution, 7.2, 7.4, 7.4, 8.4, 14.2
Poisson process, 0.2, 7.1, 7.2, 7.6, 7.8, 8.2, 14.1
Price is Right, 6.1
Prob, 2.1
parameter, 4.5
percentile, 3.5, 13.6, 13.7
posterior, 1.5
posterior distribution, 2.2, 4.2
power law, 3.4
predictive distribution, 7.8, 8.4, 8.4, 8.6, 12.8, 15.10
prevalence, 15.1, 15.3, 15.9
prior, 1.5
prior distribution, 2.2, 3.3
probability, 6.3
- conditional, 1.1
- conjoint, 1.2
probability density, 6.3
probability density function, 4.5, 6.3, 7.1
probability mass function, 2.1
process, 7.2
pseudocolor plot, 13.5
pyrosequencing, 15.1
Red Line problem, 8.1
Reddit, 4.7, 13.1
radioactive decay, 14.1
random sample, 15.4, 15.10
rarefaction curve, 15.10, 15.11
raw score, 12.3
rDNA, 15.1
regression testing, 0.2, 15.6, 15.7
renormalize, 2.2
repository, 0.3
robust estimation, 10.9
SAT, 12.1
SciPy, 0.3
Showcase, 6.1
Sivia, D.S., 9.1
Suite class, 2.5
sample bias, 15.9
sample statistics, 10.8
scaled score, 12.2
scipy, 6.4, 6.4, 10.6
serial correlation, 13.7, 13.7
simulation, 5.4, 5.5, 5.6, 13.3, 13.4, 15.10
species, 15.1, 15.9
sphere, 13.3, 13.7
standardized test, 12.1
stick, 1.7
strafing speed, 9.3
subjective prior, 1.5
subjectivity, 3.8
sudden death, 7.5
suite, 1.5
summary statistic, 6.9, 10.9, 10.11
swamping the priors, 4.3, 4.6
switch, 1.7
table method, 1.6
template method pattern, 2.7
total probability, 1.5
triangle distribution, 4.3, 11.3
trigonometry, 9.3
tumor type, 13.7
tuple, 4.4
Unseen Species problem, 15.1
Update, 2.3
uncertainty, 8.6
underflow, 10.5, 15.6
uniform distribution, 15.4
uniform distribution, 4.1, 5.6, 8.3
uninformative prior, 3.8
Vancouver Canucks, 7.1
Variability Hypothesis, 10.1
Veterans’ Benefit Administration, 13.2
volume, 13.3
Weibull distribution, 7.8
word frequency, 2.1

Index

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