Bayesian methods for the construction of robust chronologies
Bayesian modelling is a widely used, powerful approach for reducing absolute dating uncertainties in archaeological research. It is important that the methods used in chronology building are robust and reflect substantial prior knowledge. This thesis focuses on the development and evaluation of two novel, prior models: the trapezoidal phase model; and the Poisson process deposition model. Firstly, the limitations of the trapezoidal phase model were investigated by testing the model assumptions using simulations. It was found that a simple trapezoidal phase model does not reflect substantial prior knowledge and the addition of a non-informative element to the prior was proposed. An alternative parameterisation was also presented, to extend its use to a contiguous phase scenario. This method transforms the commonly-used abrupt transition model to allow for gradual changes. The second phase of this research evaluates the use of Bayesian model averaging in the Poisson process deposition model. The use of model averaging extends the application of the Poisson process model to remove the subjectivity involved in model selection. The last part of this thesis applies these models to different case studies, including
Lee, Sharen Woon Yee
Archeology ; Statistics (social sciences) ; Archaeology ; Bayesian modelling ; Chronology building ; Trapezoid ; Model averaging
Bayesian methods for the construction of robust chronologies. Bayesian modelling is a widely used, powerful approach for reducing absolute dating uncertainties in archaeological research. It is important that the methods used in chronology building are robust and reflect substantial prior knowledge. This thesis focuses on the development and evaluation of two novel, prior models: the trapezoidal phase model; and the Poisson process deposition model. Firstly, the limitations of the trapezoidal phase model were investigated by testing the model assumptions using simulations. It was found that a simple trapezoidal phase model does not reflect substantial prior knowledge and the addition of a non-informative element to the prior was proposed. An alternative parameterisation was also presented, to extend its use to a contiguous phase scenario. This method transforms the commonly-used abrupt transition model to allow for gradual changes. The second phase of this research evaluates the use of Bayesian model averaging in the Poisson process deposition model. The use of model averaging extends the application of the Poisson process model to remove the subjectivity involved in model selection. The last part of this thesis applies these models to different case studies, including
bayesian method construction robust chronology bayesian modelling widely_use powerful reduce absolute_date archaeological_research important method_use chronology build robust reflect substantial prior knowledge focus development evaluation novel prior model phase model process deposition model firstly limitation phase model investigate test model assumption use simulation found simple phase model reflect substantial prior knowledge addition non informative element prior propose alternative also_present extend use contiguous phase scenario method commonly used abrupt transition model allow gradual change second_phase evaluate use bayesian_model average process deposition model use model average extends application process model remove subjectivity involve model selection last_part applies model different case_study include