Introduction to Machine Learning with Applications in Information Security (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Cover Page
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Introduction
	1.1 Basic sampling concepts
		1.1.1 Population parameters
		1.1.2 Descriptive statistics vs. inference about a population
		1.1.3 Random sampling vs. probability sampling
	1.2 Design-based vs. model-based approach
	1.3 Populations used in sampling experiments
		1.3.1 Soil organic matter in Voorst, the Netherlands
		1.3.2 Poppy fields in Kandahar, Afghanistan
		1.3.3 Aboveground biomass in Eastern Amazonia, Brazil
		1.3.4 Annual mean air temperature in Iberia
I Probability sampling for estimating population parameters
	2 Introduction to probability sampling
		2.1 Horvitz-Thompson estimator
		2.2 Hansen-Hurwitz estimator
		2.3 Using models in design-based approach
	3 Simple random sampling
		3.1 Estimation of population parameters
			3.1.1 Population proportion
			3.1.2 Cumulative distribution function and quantiles
		3.2 Sampling variance of estimator of population parameters
		3.3 Confidence interval estimate
			3.3.1 Confidence interval for a proportion
		3.4 Simple random sampling of circular plots
			3.4.1 Sampling from a finite set of fixed circles
			3.4.2 Sampling from an infinite set of floating circles
	4 Stratified simple random sampling
		4.1 Estimation of population parameters
			4.1.1 Population proportion, cumulative distribution function, and quantiles
			4.1.2 Why should we stratify?
		4.2 Confidence interval estimate
		4.3 Allocation of sample size to strata
		4.4 Cum-root-f stratification
		4.5 Stratification with multiple covariates
		4.6 Geographical stratification
		4.7 Multiway stratification
		4.8 Multivariate stratification
	5 Systematic random sampling
		5.1 Estimation of population parameters
		5.2 Approximating the sampling variance of the estimator of the mean
	6 Cluster random sampling
		6.1 Estimation of population parameters
		6.2 Clusters selected with probabilities proportional to size, without replacement
		6.3 Simple random sampling of clusters
		6.4 Stratified cluster random sampling
	7 Two-stage cluster random sampling
		7.1 Estimation of population parameters
		7.2 Primary sampling units selected without replacement
		7.3 Simple random sampling of primary sampling units
		7.4 Stratified two-stage cluster random sampling
	8 Sampling with probabilities proportional to size
		8.1 Probability-proportional-to-size sampling with replacement
		8.2 Probability-proportional-to-size sampling without replacement
			8.2.1 Systematic pps sampling without replacement
			8.2.2 The pivotal method
	9 Balanced and well-spread sampling
		9.1 Balanced sampling
			9.1.1 Balanced sample vs. balanced sampling design
			9.1.2 Unequal inclusion probabilities
			9.1.3 Stratified random sampling
			9.1.4 Multiway stratification
		9.2 Well-spread sampling
			9.2.1 Local pivotal method
			9.2.2 Generalised random-tessellation stratified sampling
		9.3 Balanced sampling with spreading
	10 Model-assisted estimation
		10.1 Generalised regression estimator
			10.1.1 Simple and multiple regression estimators
			10.1.2 Penalised least squares estimation
			10.1.3 Regression estimator with stratified simple random sampling
		10.2 Ratio estimator
			10.2.1 Ratio estimators with stratified simple random sampling
			10.2.2 Poststratified estimator
		10.3 Model-assisted estimation using machine learning techniques
			10.3.1 Predicting with a regression tree
			10.3.2 Predicting with a random forest
		10.4 Big data and volunteer data
	11 Two-phase random sampling
		11.1 Two-phase random sampling for stratification
		11.2 Two-phase random sampling for regression
	12 Computing the required sample size
		12.1 Standard error
		12.2 Length of confidence interval
			12.2.1 Length of confidence interval for a proportion
		12.3 Statistical testing of hypothesis
			12.3.1 Sample size for testing a proportion
		12.4 Accounting for design effect
		12.5 Bayesian sample size determination
			12.5.1 Bayesian criteria for sample size computation
			12.5.2 Mixed Bayesian-likelihood approach
			12.5.3 Estimation of population mean
			12.5.4 Estimation of a population proportion
	13 Model-based optimisation of probability sampling designs
		13.1 Model-based optimisation of sampling design type and sample size
			13.1.1 Analytical approach
			13.1.2 Geostatistical simulation approach
			13.1.3 Bayesian approach
		13.2 Model-based optimisation of spatial strata
	14 Sampling for estimating parameters of domains
		14.1 Direct estimator for large domains
		14.2 Model-assisted estimators for small domains
			14.2.1 Regression estimator
			14.2.2 Synthetic estimator
		14.3 Model-based prediction
			14.3.1 Random intercept model
			14.3.2 Geostatistical model
		14.4 Supplemental probability sampling of small domains
	15 Repeated sample surveys for monitoring population parameters
		15.1 Space-time designs
		15.2 Space-time population parameters
		15.3 Design-based generalised least squares estimation of spatial means
			15.3.1 Current mean
			15.3.2 Change of the spatial mean
			15.3.3 Temporal trend of the spatial mean
			15.3.4 Space-time mean
		15.4 Case study: annual mean daily temperature in Iberia
			15.4.1 Static-synchronous design
			15.4.2 Independent synchronous design
			15.4.3 Serially alternating design
			15.4.4 Supplemented panel design
			15.4.5 Rotating panel design
			15.4.6 Sampling experiment
		15.5 Space-time sampling with stratified random sampling in space
II Sampling for mapping
	16 Introduction to sampling for mapping
		16.1 When is probability sampling not required?
		16.2 Sampling for simultaneously mapping and estimating means
		16.3 Broad overview of sampling designs for mapping
	17 Regular grid and spatial coverage sampling
		17.1 Regular grid sampling
		17.2 Spatial coverage sampling
		17.3 Spatial infill sampling
	18 Covariate space coverage sampling
		18.1 Covariate space infill sampling
		18.2 Performance of covariate space coverage sampling in random forest prediction
	19 Conditioned Latin hypercube sampling
		19.1 Conditioned Latin hypercube infill sampling
		19.2 Performance of conditioned Latin hypercube sampling in random forest prediction
	20 Spatial response surface sampling
		20.1 Increasing the sample size
		20.2 Stratified spatial response surface sampling
		20.3 Mapping
	21 Introduction to kriging
		21.1 Ordinary kriging
		21.2 Block-kriging
		21.3 Kriging with an external drift
		21.4 Estimating the semivariogram
			21.4.1 Method-of-moments
			21.4.2 Maximum likelihood
		21.5 Estimating the residual semivariogram
			21.5.1 Iterative method-of-moments
			21.5.2 Restricted maximum likelihood
	22 Model-based optimisation of the grid spacing
		22.1 Optimal grid spacing for ordinary kriging
		22.2 Controlling the mean or a quantile of the ordinary kriging variance
		22.3 Optimal grid spacing for block-kriging
		22.4 Optimal grid spacing for kriging with an external drift
		22.5 Bayesian approach
	23 Model-based optimisation of the sampling pattern
		23.1 Spatial simulated annealing
		23.2 Optimising the sampling pattern for ordinary kriging
		23.3 Optimising the sampling pattern for kriging with an external drift
		23.4 Model-based infill sampling for ordinary kriging
		23.5 Model-based infill sampling for kriging with an external drift
	24 Sampling for estimating the semivariogram
		24.1 Nested sampling
		24.2 Independent sampling of pairs of points
		24.3 Optimisation of sampling pattern for semivariogram estimation
			24.3.1 Uncertainty about semivariogram parameters
			24.3.2 Uncertainty about the kriging variance
		24.4 Optimisation of sampling pattern for semivariogram estimation and mapping
		24.5 A practical solution
	25 Sampling for validation of maps
		25.1 Map quality indices
			25.1.1 Estimation of map quality indices
		25.2 Real-world case study
			25.2.1 Estimation of the population mean error and mean squared error
			25.2.2 Estimation of the standard error of the estimator of the population mean error and mean squared error
			25.2.3 Estimation of model efficiency coefficient
			25.2.4 Statistical testing of hypothesis about population ME and MSE
	26 Design-based, model-based, and model-assisted approach for sampling and inference
		26.1 Two sources of randomness
		26.2 Identically and independently distributed
		26.3 Bias and variance
		26.4 Effective sample size
		26.5 Exploiting spatial structure in design-based approach
		26.6 Model-assisted vs. model-dependent
A Answers to exercises
Bibliography
Index
Copyright
Title Page
Dedication
Contents
Chapter 1: ‘I’m thinking’ – Oh, but are you?
Chapter 2: Renegade perception
Chapter 3: The Pushbacker sting
Chapter 4: ‘Covid’: The calculated catastrophe
Chapter 5: There is no ‘virus’
Chapter 6: Sequence of deceit
Chapter 7: War on your mind
Chapter 8: ‘Reframing’ insanity
Chapter 9: We must have it? So what is it?
Chapter 10: Human 2.0
Chapter 11: Who controls the Cult?
Chapter 12: Escaping Wetiko
Postscript
Appendix: Cowan-Kaufman-Morell Statement on Virus Isolation
Bibliography
Index