Search results for " menetelmä"
showing 10 items of 273 documents
Effect of macro and micro nutrients addition during anaerobic mono-digestion of grass silage in leach-bed reactors
2019
The effect of macro- (NH4Cl) (set I) and micro-nutrients (Fe, Ni, Co and Mo) (set II) addition on chemical oxygen demand (COD) solubilisation during anaerobic mono-digestion of grass silage was investigated in two sets of leach bed reactor experiments at 35°C. Results showed that addition of NH4Cl and micro-nutrients improved COD solubilisation by 18% (0.56 g SCOD g−1 volatile solids) and 7% (0.45 g SCOD g−1 VS), respectively than control. About 20–50% of the added micro-nutrients were bioavailable in the produced leachates, while the rest (50–80%) were adsorbed onto the grass silage. Results of biological methane potential assays showed that, specific methane yields of grass silage were im…
Predictors of school students’ leisure-time physical activity : An extended trans-contextual model using Bayesian path analysis
2021
Abstract Background:The trans-contextual model (TCM) has been applied to identify the determinants of leisure-time physical activity participation in secondary school students. In the current study, the TCM was extended to include additional constructs that represent non-conscious, implicit processes that lead to leisure-time physical activity participation alongside the motivational and social cognition constructs from the TCM. The current study used baseline and follow-up data from an intervention study to test the extended TCM.Methods:The current study adopted a two-wave prospective design. Secondary-school students (N = 502) completed measures of perceived autonomy support from physical…
Bayesian subcohort selection for longitudinal covariate measurements in follow‐up studies
2022
We propose an approach for the planning of longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudinal measurements due to financial limitations, and study how a subset of the cohort should be selected optimally, in order to obtain precise estimates of covariate effects in a survival model. In our approach, the study will be designed sequentially utilizing the data collected in previous measurements of the individuals as prior information. We propose using a Bayesian optimality criterion in the subcohort selections, which is compared with simple random sampling using simulated and real follo…
Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance
2017
We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an approximate reversible chain and subsequent IS weighting, and a standard MCMC estimator, based on an exact reversible chain. Essentially, we relax the criterion of the Peskun type covariance ordering by considering two different invariant probabilities, and obtain, in place of a strict ordering of asymptotic variances, a bound of the asymptotic variance of IS by that of the direct MCMC. Simple examples show that IS can have arbitrarily better or worse asymptotic variance than Metropolis-Hastings and delayed-acceptanc…
Estimating the causal effect of timing on the reach of social media posts
2022
AbstractModern companies regularly use social media to communicate with their customers. In addition to the content, the reach of a social media post may depend on the season, the day of the week, and the time of the day. We consider optimizing the timing of Facebook posts by a large Finnish consumers’ cooperative using historical data on previous posts and their reach. The content and the timing of the posts reflect the marketing strategy of the cooperative. These choices affect the reach of a post via a dynamic process where the reactions of users make the post more visible to others. We describe the causal relations of the social media publishing in the form of a directed acyclic graph, …
Efficient spatial designs using Hausdorff distances and Bayesian optimization
2021
An iterative Bayesian optimisation technique is presented to find spatial designs of data that carry much information. We use the decision theoretic notion of value of information as the design criterion. Gaussian process surrogate models enable fast calculations of expected improvement for a large number of designs, while the full-scale value of information evaluations are only done for the most promising designs. The Hausdorff distance is used to model the similarity between designs in the surrogate Gaussian process covariance representation, and this allows the suggested algorithm to learn across different designs. We study properties of the Bayesian optimisation design algorithm in a sy…
Bayesian Modeling of Sequential Discoveries
2022
We aim at modelling the appearance of distinct tags in a sequence of labelled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarised via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modelling by directly specifying the probability of a new discovery, therefore allowing for flexible specifications. The asymptotic behavior and finite sample properties of such an approach are extensively studied. Interestingly, our enlarged class of sequential processes includes highly tr…
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
2021
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package accommodates also discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package implements also a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers …
Random walk approximation of BSDEs with H{\"o}lder continuous terminal condition
2018
In this paper, we consider the random walk approximation of the solution of a Markovian BSDE whose terminal condition is a locally Hölder continuous function of the Brownian motion. We state the rate of the L2-convergence of the approximated solution to the true one. The proof relies in part on growth and smoothness properties of the solution u of the associated PDE. Here we improve existing results by showing some properties of the second derivative of u in space. peerReviewed
Lärares och elevers uppfattningar om och erfarenheter av kinestetiska metoder i svensk- och tyskundervisningen på högstadiet
2016
Tämän tutkimuksen tarkoituksena oli selvittää, millaisia käsityksiä ja kokemuksia yläkoulun opettajilla ja oppilailla on toiminnallisista työtavoista ruotsin ja saksan kielen opetuksessa. Tutkimuksessa selvitettiin, käyttävätkö kieltenopettajat toiminnallisuutta opetuksessaan ja millä tavoin. Lisäksi kartoitettiin opettajien syitä käyttää toiminnallisia työtapoja kielten opetuksessa. Tutkimusaineisto kerättiin teemahaastatteluiden ja kyselylomakkeen avulla. Aineiston analyysissä käytettiin sekä kvalitatiivisia että kvantitatiivisia menetelmiä. Tutkimuksen tulokset osoittivat, että tutkimukseen osallistuneet opettajat suhtautuvat positiivisesti toiminnallisiin työtapoihin ja toiminnallisuutt…