Search results for " menetelmä"

Assemblage of art, discourse and ice hockey : designing knowledge about work

2021

This article examines speculative design's capacity to co‐produce knowledge about contradictions and potentialities of work in professional ice hockey. Building on the Deleuzian concept of assemblage, speculative design has been used for two purposes: (a) to bring together the perspectives of art, anthropology, discourse studies, and professional sports in co‐constructing knowledge about hockey work; and (b) to analyze and present the key findings of an ethnography on hockey work through an art exhibition of speculative hockey memorabilia. As such, these art pieces showed the intertwined relationships of material, discursive, and affective aspects in hockey work as well as the multiplicity …

Statistical models and inference for spatial point patterns with intensity-dependent marks

2009

Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy

2019

Maintenance chemotherapy with oral 6-mercaptopurine and methotrexate remains a cornerstone of modern therapy for acute lymphoblastic leukaemia. The dosage and intensity of therapy are based on surrogate markers such as peripheral blood leukocyte and neutrophil counts. Dosage based leukocyte count predictions could provide support for dosage decisions clinicians face trying to find and maintain an appropriate dosage for the individual patient. We present two Bayesian nonlinear state space models for predicting patient leukocyte counts during the maintenance therapy. The models simplify some aspects of previously proposed models but allow for some extra flexibility. Our second model is an ext…

Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

2020

We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelisation and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the sug…

Yliopistotutkintojen määrän ennustaminen Bayes-mallilla

2017

Tämän tutkielman tarkoituksena on kehittää prediktiivinen malli, jolla ennustetaan Jyväskylän yliopiston matemaattis-luonnontieteellisessä tiedekunnassa lähivuosina suoritettavien luonnontieteiden kandidaatin ja filosofian maisterin tutkintojen lukumääriä. Mallin estimointiin käytettävä aineisto koostuu kolmesta osasta: vuosina 1996–2004 tiedekunnassa aloittaneet opiskelijat, vuosina 2005–2015 tiedekunnassa alemmasta korkeakoulututkinnosta aloittaneet opiskelijat ja vuosina 2005–2016 tiedekunnassa ylemmästä korkeakoulututkinnosta aloittaneet opiskelijat. Jokaiselle aineiston osalle sovitetaan omat toisistaan riippumattomat osamallit. Tutkintoennusteet saadaan ennustamalla aineistoon kuuluvi…

Proton Direct Ionization in Sub-Micron Technologies: Numerical Method for RPP Parameter Extraction

2022

This work introduces a numerical method to iteratively extract parameters of a rectangular parallelepiped (RPP) sensitive volume (SV) from experimental proton direct ionization SEU data. The method combines two separate numerical models. The first model estimates the average LET values for energetic ions, including protons and also heavy ions, in elemental solid targets. The second model describes the statistical variance in the energy deposition events of projectile-induced primary ionization within a RPP shaped target volume. To benchmark the method, simulated cross-section values based on RPP parameters derived with this method are compared with literature data from four SRAM devices. Th…

BEM-Based Magnetic Field Reconstruction by Ensemble Kálmán Filtering

2022

Abstract Magnetic fields generated by normal or superconducting electromagnets are used to guide and focus particle beams in storage rings, synchrotron light sources, mass spectrometers, and beamlines for radiotherapy. The accurate determination of the magnetic field by measurement is critical for the prediction of the particle beam trajectory and hence the design of the accelerator complex. In this context, state-of-the-art numerical field computation makes use of boundary-element methods (BEM) to express the magnetic field. This enables the accurate computation of higher-order partial derivatives and local expansions of magnetic potentials used in efficient numerical codes for particle tr…

Menetelmiä regressiomallin estimointiin kompleksisessa otanta-asetelmassa : sovellus PISA 2009 -aineistoon

2012

Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies

2018

We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…

A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

2018

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…