Bayesian Population Reconstruction

Current methods for reconstructing human populations of the past by age and sex are deterministic or do not formally account for measurement error. I propose Bayesian reconstruction, a method for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data, that incorporates measurement error. Expert opinion is incorporated formally through informative priors. Inference is based on joint posterior probability distributions which yield fully probabilistic interval estimates. Previous methods of reconstruction did not account for measurement error, or imposed fixed age-patterns on some parameters. It is designed for the kind of data commonly collected in modern demographic surveys and censuses. Population dynamics over the period of reconstruction are modeled by embedding formal demographic accounting relationships in a Bayesian hierarchical model. Informative priors are specified for vital rates, migration rates, population counts at baseline, and their respective measurement error variances. Statistical properties of Bayesian reconstruction are investigated through simulation and sensitivity analyzes. The method is applied to real data from Burkina Faso, Laos, New Zealand, Sri Lanka, Thailand and India, demonstrating its applicability to developing and developed countries. It can also be used to compare model life tables. When full populations are reconstructed, probabilistic estimates of sex ratios, such as the sex ratio at birth and sex ratios of mortality, can also be obtained. Bayesian reconstruction is implemented in the R package popReconstruct.

Emphasizing model choice and model averaging, this book presents up-to-date Bayesian methods for analyzing complex ecological data. It provides a basic introduction to Bayesian methods that assumes no prior knowledge. The book includes detailed descriptions of methods that deal with covariate data and covers techniques at the forefront of research, such as model discrimination and model averaging. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book's website.

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty. The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com. "This book will be welcome for the scientific community of forecasters...as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'études démographiques

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research. Encouraging interdisciplinary research, this book introduces state-of-the-art phylogenetics to the Bayesian statistical community and, likewise, presents state-of-the-art Bayesian statistics to the phylogenetics community. The book emphasizes model selection, reflecting recent interest in accurately estimating marginal likelihoods. It also discusses new approaches to improve mixing in Bayesian phylogenetic analyses in which the tree topology varies. In addition, the book covers divergence time estimation, biologically realistic models, and the burgeoning interface between phylogenetics and population genetics.

This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major phases, reflected in the three main sections of this book. The first phase involves transcribing and digitizing the data while structuring the information in a meaningful and efficient way. In the second phase, records that refer to the same person or group of persons are identified by a process of linkage. In the third and final phase, the information on an individual is combined into a reconstruction of their life course. The studies and examples in this book originate from a range of countries, each with its own cultural and administrative characteristics, and from medieval charters through historical censuses and vital registration, to the modern issue of privacy preservation. Despite the diverse places and times addressed, they all share the study of fundamental issues when it comes to model reasoning for population reconstruction and the possibilities and limitations of information technology to support this process. It is thus not a single discipline that is involved in such an endeavor. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources, and the possible interpretations of information. The availability of big data from digitized archives and the need for complex analyses to identify individuals calls for the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of the digital humanities, and will hopefully offer a source of inspiration for future investigations.

"Near time" -an interval that spans the last 100,000 years or so of earth history-qualifies as a remarkable period for many reasons. From an anthropocentric point of view, the out standing feature of near time is the fact that the evolution, cultural diversification, and glob al spread of Homo sapiens have all occurred within it. From a wider biological perspective, however, the hallmark of near time is better conceived of as being one of enduring, repeat ed loss. The point is important. Despite the sense of uniqueness implicit in phrases like "the biodiversity crisis," meant to convey the notion that the present bout of extinctions is by far the worst endured in recent times, substantial losses have occurred throughout near time. In the majority of cases, these losses occurred when, and only when, people began to ex pand across areas that had never before experienced their presence. Although the explana tion for these correlations in time and space may seem obvious, it is one thing to rhetori cally observe that there is a connection between humans and recent extinctions, and quite another to demonstrate it scientifically. How should this be done? Traditionally, the study of past extinctions has fallen largely to researchers steeped in such disciplines as paleontology, systematics, and paleoecology. The evaluation of future losses, by contrast, has lain almost exclusively within the domain of conservation biolo gists. Now, more than ever, there is opportunity for overlap and sharing of information.

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: • Addresses the theoretical aspects of the field • Advises on how to prepare and perform phylogenetic analysis • Helps with interpreting analyses and visualisation of phylogenies • Describes the software architecture • Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users – from those using phylogenetic tools, to computational biologists and Bayesian statisticians.

This book uses data from 26 Anglican to provide information about fertility, morality and nuptiality in the past.

Abstract: "Uncovering the haplotypes of single nucleotide polymorphisms and their population demography is essential for many biological and medical applications. Methods for haplotype inference developed thus far -- including those based on approximate coalescence, finite mixtures, and maximal parsimony -- often bypass issues such as unknown complexity of haplotype-space and demographic structures underlying multi-population genotype data. In this paper, we propose a new class of haplotype inference models based on a nonparametric Bayesian formalism built on the Dirichlet process, which represents a tractable surrogate to the coalescent process underlying population haplotypes and offers a well-founded statistical framework to tackle the aforementioned issues. Our proposed model, known as a hierarchical Dirichlet process mixture, is exchangeable, unbounded, and capable of coupling demographic information of different populations for posterior inference of individual haplotypes, the size and configuration of haplotype ancestor pools, and other parameters of interest given genotype data. The resulting haplotype inference program, Haploi, is readily applicable to genotype sequences with thousands of SNPs, at a time-cost often two-orders of magnitude less than that of the state-of-the-art PHASE program, with competitive and sometimes superior performance. Haploi also significantly outperforms several other extant algorithms on both simulated and realistic data."