A Bayesian reconstruction of historical population in Finland, 1647-1850

Abstract This article provides a novel method to estimate historical population development. We review the previous literature on historical population time series estimates and propose a general outline to address the well-known methodological problems. We use a Bayesian hierarchical time series model that allows us to integrate parish level dataset and prior population information in a coherent manner. The procedure provides us with model-based posterior intervals for the final population estimates.

Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest

Abstract Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene while OoI identification uncovers distinct objects in the scene that attract user attention.

Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

Abstract Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations.

Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Abstract Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods.

Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data

Abstract Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group.