Development of project management procedures for customer project business

Objective of the thesis is to develop project management procedure for chilled beam projects. In organization is recognized that project management techniques could help in large and complex projects. Information sharing have been challenging in projects, so improvement of information sharing is one key topic of the thesis. Academic researches and literature are used to find suitable project management theories and methods. Main theories are related to phases of the project and project management tools. Practical knowledge of project management is collected from two project business oriented companies. Project management tools are chosen and modified to fulfill needs of the beam projects. Result of the thesis is proposed project management procedure, which includes phases of the chilled beam projects and project milestones. Project management procedure helps to recognize the most critical phases of the project and tools help to manage information of the project. Procedure increases knowledge of the project management techniques and tools. It also forms coherent project management working method among the chilled beam project group.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.