We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.

Conjoint choice experiments are used widely in marketing to study consumer preferences amongst alternative products. We develop a class of choice models, belonging to the class of Poisson race models, that describe a ‘random utility’ which lends itself to a process-based description of choice. The models incorporate a dependence structure which captures the relationship between the attributes of the choice alternatives and which appropriately moderates the randomness inherent in the race. The new models are applied to conjoint choice data and are shown to have performance markedly superior to that of independent Poisson race models and of the multinomial logit model.

The response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

Love (2018) misunderstands some concepts of Bayesian analysis and the data from Naranjo and Urías. The cross-dating of El Ujuxte needs to be reevaluated with the publication of its ceramic data.

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

We estimated the abundance of Antillean manatees (Trichechus manatus manatus) through a large-scale project conducted in 2010 in north-eastern Brazil and evaluated the efficacy of an aerial survey for conservation purposes. Two observers conducted the survey via flights that maintained an altitude of 150 m and an air speed of 140 km h−1, covering over 2590.2 km2 of the coastline. Strip transects were flown in a zigzag pattern. A total of 67 manatees (on- and off-effort) were recorded in 55 sightings. Historical published records of occurrence were formally incorporated using a Bayesian approach. We estimated the manatee population for north-eastern Brazil in the form of a posterior distribution with a mean of 1104 individuals and a 95% posterior probability interval ranging from 485 to 2221 individuals, which indicates high uncertainty. More large-scale studies in the region are warranted to understand temporal trends, in addition to further studies in hotspot areas, with smaller spatial scales, to reduce the coefficient of variation and to allow the use of improved techniques for monitoring the manatees. A greater emphasis on species-specific characteristics and methods to enhance detection probability (e.g. dual observers) are also recommended. The conditions prevailing along the study area were not conducive to aerial surveillance; thus, the results are not a precise estimate of the manatee population. However, these highlight the importance of conservation efforts for the Antillean manatee, considered the most endangered aquatic mammal in Brazil.

We use economic indicators to improve the prediction of the number of incurred but not recorded disability insurance claims, assuming that there is a link between the number of claims and the chosen economic indicators. We propose a Bayesian model where we model the claims development in three directions: along incurred periods, recording lag periods and calendar periods. A stochastic model of the economic indicators is incorporated into the calendar period development direction. Thus we allow for the impact of the economic environment on the number of claims. Applying the proposed model to data, we illustrate how the inclusion of economic indicators affects the prediction of the number of incurred but not recorded disability claims.

The Bayesian model presented in this article is the first attempt to produce a chronological framework for the Iron Age in the Levant, using radiocarbon dating alone. The model derives from 339 determinations on 142 samples taken from 38 strata at 18 sites. The framework proposes six ceramic phases and six transitions which cover c. 400 years, between the late twelfth and mid eighth centuries BC. It furnishes us with a new scientific backbone for the history of Iron Age Levant.

The article is supported by an online supplement which can be found in at http://antiquity.ac.uk/projgall/finkelstein324

A mapping and navigation system based on certainty grids for an autonomous mobile robot operating in unknown environment is described. The system uses sonar range data to build a map of the robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world by experiment.

The collective risk model for the insurance claims is considered. The objective is to estimate a premium which is defined as a functional H specified up to an unknown parameter θ (the expected number of claims). Four principles of calculating a premium are applied. The Bayesian methodology, which combines the prior knowledge about a parameter θ with the knowledge in the form of a random sample is adopted. Two loss functions (the square-error loss function and the asymmetric loss function LINEX) are considered. Some uncertainty about a prior is assumed by introducing classes of priors. Considering one of the concepts of robust procedures the posterior regret Γ-minimax premiums are calculated, as an optimal robust premiums. A numerical example is presented.

Mathematical models have been proposed for oil exploration and other kinds of search. They can be used to estimate the amount of undiscovered resources or to investigate optimal stopping times for the search. Here we consider a continuous search for hidden objects using a model which represents the number and values of the objects by mixtures of Poisson processes. The flexibility of the model and its complexity depend on the number of components in the mixture. In simple cases, optimal stopping rules can be found explicitly and more general qualitative results can sometimes be obtained.

Following previous formulations of a model of qualitative analysis of twin population data independent of zygosity, a new Bayesian approach has been developed. The present model can be applied to any qualitative genetic trait in twin population data, provided no specific source of variation be introduced by the twin condition, and allows not only estimation of the frequencies of mono- and dizygosity as well as the gene frequencies, but also verification of the trait's mode of inheritance.