Chronological Modelling Examples:

We specialize in using Bayesian Chronological models and Summed Probability Distributions (SPDs) of chronological data to refine temporal estimates and summarize trends in aggregate data. See our examples below for how these methods may apply to your project.

What is Bayesian?

"Bayesian" refers to a branch of statistics broadly based on Baye's Theorem, which can be thought of as a kind of conditional probability.

In practice, this means that contextual information about data and their relationships is used to refine the outcomes of analysis.

Refining Age Estimates and Quantifying Uncertainty of Chronological Data

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Rate of Deposition

or Age-Depth Modelling

Use of stratigraphic and depth information to model rates of deposition and simulate ages of undated portions.

Modelling beginning, end, central tendancy and possible overlap among cultural phases, traditions, artifact styles, etc.

We can use data from absolute dating methods, artifact styles, historical sources and much more to better understand the beginning, end, central tendency of cultural phenomenon (such as cultural phases, artifact styles, regional occupation patterns) as well as the likelihood and degree to which these phenomena interacted with others.

see Brown et al. 2022 for example

Summed Probability Distributions of Aggregate Data

As seen in numerous archaeological publications (see Crema 2022 for good summary), we use SPDs to summarize trends in chronological data which can be used to model fluctuations in population levels, compare regional settlement patterns, land-use characteristics, and compare relative abundance of features, sites and artifact types.

These also make excellent data summary graphs.

See examples in our publications ****