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Estimates of historical world population
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Related lectures (14)
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Population Growth and Economic Transition
Examines historical population and economic growth, challenges of an extractive economy, and the need for sustainable development.
Population Growth and Sustainability
Explores the historical evolution and determinants of world population growth, emphasizing its implications for sustainable development.
Conditional Expectation: Properties and Examples
Explores conditional expectation properties, variance, and examples in practical applications.
Economic Globalisation: Key Facts and Geography
Explores key facts and geography shaping economic globalisation, human population history, and climate change.
Extreme Statistics: Threshold Models
Covers the theory and applications of extreme statistics, focusing on threshold models for analyzing extremes of time series.
Extreme Value Theory: Point Processes
Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.
Financial Decision Making: Cost-Benefit Analysis in Projects
Covers financial decision making through cost-benefit analysis in public projects, focusing on investment viability and the implications of interest rates.
Urban Planning in African Cities
Discusses urban planning in African cities, focusing on Brazzaville and population estimates for 2035.
Mapping and Colouring: Poisson Processes
Covers the theorems of superposition and colouring for Poisson processes.
Transformations of Variables
Covers the calculation of cumulative distribution functions and probability density functions for transformed random variables.
Population Growth and Resource Consumption
Explores population growth models, resource consumption, and real-world case studies.
Estimating R
Covers the topic of estimating R and calculating probabilities and expected waiting times.
Estimating R: Marginal and Conditional Distributions
Covers the estimation of R using bivariate normal distributions and explores the marginal and conditional distributions of X₁ and X₂.
Poisson Process Mapping
Explains how q = rw defines a Poisson process and its intensity.
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