In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines component be flexible extension of base model. Proper priors are defined penalise complexity induced by deviating from simpler and formulated after input user-defined scaling parameter that component, both in univariate multivariate case. These invariant reparameterisations, have connection Jeffreys’ priors, designed support...

In recent years, disease mapping studies have become a routine application within geographical epidemiology and are typically analysed Bayesian hierarchical model formulation. A variety of formulations for the latent level been proposed but all come with inherent issues. classical BYM (Besag, York Mollié) model, spatially structured component cannot be seen independently from unstructured component. This makes prior definitions hyperparameters two random effects challenging. There...

This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making use of continuously specified Gaussian random field. We show that sufficiently smooth field prior distributions, the approximation can converge with arbitrarily high order, whereas an based counting process partition domain achieves only first-order convergence. results improve upon general theory convergence stochastic partial...

This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. enables us use integrated nested Laplace approximation considerably speed up the inferential task. In addition, methods model comparison assessment facilitate modelling process. The performance of approach is assessed simulation...

Summary We highlight an emerging statistical method, integrated nested Laplace approximation ( INLA ), which is ideally suited for fitting complex models to many of the rich spatial data sets that ecologists wish analyse. method nevertheless provides very exact estimates. In this article, we describe methodology highlighting where it offers opportunities drawing inference from (spatial) ecological would previously have been too make practical model feasible. use fit a joint pattern formed by...

The ocean migration of 16 post-spawned adult Atlantic salmon [Salmo salar L.] from the Miramichi River, Canada, tagged concurrently with pop-up satellite archival tags and acoustic transmitters was reconstructed using a Hidden Markov Model. Individuals exclusively utilized areas within Gulf St Lawrence Labrador Sea, showed little overlap known distributions European stocks. During migration, individuals were generally associated surface waters spent >67% time in upper 10 m water...

The autoregressive (AR) process of order p (AR( )) is a central model in time series analysis. A Bayesian approach requires the user to define prior distribution for coefficients AR( ) model. Although it easy write down some prior, not at all obvious how understand and interpret distribution, ensure that behaves according users' knowledge. In this article, we problem using recently developed ideas penalised complexity (PC) priors. These have important properties like robustness invariance...

Solar Orbiter provides dust detection capability in inner heliosphere, but estimating physical properties of detected from the collected data is far straightforward. First, a model for collection considering Poisson process formulated. Second, it shown that on hyperbolic orbits responsible majority detections with Orbiter's Radio and Plasma Waves (SolO/RPW). Third, counts fitted to SolO/RPW parameters are inferred, namely: radial velocity, meteoroids predominance, solar radiation pressure...

Fractional Gaussian noise (fGn) is a stationary stochastic process used to model antipersistent or persistent dependency structures in observed time series. Properties of the autocovariance function fGn are characterised by Hurst exponent ( H ), which, Bayesian contexts, typically has been assigned uniform prior on unit interval. This paper argues why unreasonable and introduces use penalised complexity (PC) for . The PC computed penalise divergence from special case white invariant...

In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines component be flexible extension of base model. Proper priors are defined penalise complexity induced by deviating from simpler and formulated after input user-defined scaling parameter that component, both in univariate multivariate case. These invariant reparameterisations, have connection Jeffreys' priors, designed support...

Earth’s global surface temperature shows variability on an extended range of temporal scales and satisfies emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature the observed fluctuations, but inherent property response to radiative forcing, principle links fast slow climate responses. It provides bridge between decadal- centennial-scale fluctuations in instrumental record, millennial-scale equilibration following perturbations balance. In particular,...

A healthy diet can decrease the risk of several lifestyle diseases. From studying health effects single foods, research now focuses on examining complete diets and dietary patterns reflecting combined intake different foods. The main goals current study were to identify then investigate how these differ in terms sex, age, educational level physical activity (PAL) a general Nordic population.We used data from seventh survey population-based Tromsø Study Norway, conducted 2015-2016. included...

Abstract. Deterministic Bayesian inference for latent Gaussian models has recently become available using integrated nested Laplace approximations (INLA). Applying the INLA‐methodology, marginal estimates elements of field can be computed efficiently, providing relevant summary statistics like posterior means, variances and pointwise credible intervals. In this article, we extend use INLA to joint present an algorithm derive analytical simultaneous bands subsets field. The is based on...

We estimate the weekly excess all-cause mortality in Norway and Sweden, years of life lost (YLL) attributed to COVID-19 significance displacement. computed expected by taking into account declining trend seasonality two countries over past 20 years. From Sweden 2019/20, we estimated YLL using expectancy different age groups. adjusted this for possible displacement an auto-regressive model year-to-year variations mortality. found that epidemic year, July 2019 2020, was 517 (95%CI = (12,...

Abstract. Reliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing risk dangerous anthropogenic climate change. We present statistical foundations an observation-based approach using a stochastic linear model that consistent with long-range temporal dependence observed in variability. have incorporated latent Gaussian modeling framework, which allows use integrated nested Laplace approximations (INLAs) perform full...

Abstract Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength dependence. This is typically addressed using state‐space model approaches, which integrate error population process estimates. Such seldom include an explicit link between procedures true abundance, common in capture–recapture settings. However, many proposed estimate abundance presence capture heterogeneity incomplete...

Abstract Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or with identifiable features, less attention being paid small mammals, including rodents. Here we investigate the suitability camera‐trap‐based abundance indices monitor population dynamics in two species voles key functions boreal Arctic ecosystems, known for their high‐amplitude cycles. targeted...

Studies of spatial population synchrony constitute a central approach for understanding the drivers ecological dynamics. Recently, identifying impacts climate change has emerged as new important focus in studies. However, while it is well known that climatic seasonality and sequential density dependence influences local dynamics, role season-specific shaping large-scale not received attention. Here, we present widely applicable analytical protocol allows us to account both season geographic...

Norway is one of the leading ocean-based food production nations. Its seafood industry comprises wild-capture fisheries and farmed fish production. Both industries play a provisional role but also contribute to economic development country help sustain coastal communities, particularly, in Northern Norway. Coastal fishery has been staple for centuries, while aquaculture complemented this region only approximately 40 years ago. To date, there limited knowledge on how two co-developed While...

Abstract. The presented method called Significant Non‐stationarities, represents an exploratory tool for identifying significant changes in the mean, variance, and first‐lag autocorrelation coefficient of a time series. are detected on different scales. statistical inference each scale is based accurate approximation probability distribution, using test statistics being ratios quadratic forms. No assumptions concerning autocovariance function series made as dependence structure estimated...