Search results for "todennäköisyyslaskenta"
showing 10 items of 10 documents
Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty
2022
Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel intera…
Wattsin ja Strogatzin satunnaisverkkomallin klusteroituneisuus
2015
Duncan J. Watts ja Steven H. Strogatz määrittivät vuonna 1998 verkon klusterointikertoimen, joka on yleisesti käytetty tilastollinen mitta verkon klusteroituneisuuden analysointiin. Esimerkiksi sosiaalisen verkoston henkilöiden tuttavapiireillä on taipumus klusteroitua siten, että monet henkilön tuttavista ovat myös keskenään tuttavia. Verkon klusterointikerroin laskee keskiarvoisen murtoluvun henkilön tuttavapareista, jotka ovat myös keskenään tuttavia. Alain Barrat ja Martin Weigt sekä Mark E. J. Newman, Duncan J. Watts ja Steven H. Strogatz määrittävät verkon transitiivisuuskertoimen vaihtoehtoisena klusterointikertoimena. Sosiaalisessa verkostossa se laskee millä todennäköisyydellä kaks…
Bayesian semiparametric long memory models for discretized event data
2020
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent process is modeled by a smooth Gaussian process and a fractional Brownian motion by assuming an additive structure. We develop a Bayesian approach to inference using Markov chain Monte Carlo and show g…
Riskien havainnollistamisohjelmisto RiskDemo
2018
RiskDemo on vapaasti käytettävä ohjelmistotyökalu, joka on tarkoitettu havainnollistamaan riskejä todennäköisyyslaskennan ja tilastotieteen näkökulmasta. Ohjelmalla voidaan havainnollistaa riskejä monenlaisten graafisten esitysten, taulukoiden ja riskilukujen avulla. Tällä hetkellä sillä voidaan havainnollistaa demografiaa, korko- ja osakesijoittamista sekä klassista vararikkoteoriaa. Tässä artikkelissa esitellään RiskDemo-ohjelmiston ominaisuudet ja opastetaan sen käyttämiseen riskien havainnollistamisessa. Lukija voi RiskDemon avulla tutustua moniin riskienhallinnan avainkäsitteisiin havainnollisessa ja konkreettisessa muodossa. peerReviewed
Odottelua pysäkillä
2015
Probabilistic analysis of sorting algorithms : lecture notes
2004
Theoretical and methodological aspects of MCMC computations with noisy likelihoods
2018
Approximate Bayesian computation (ABC) [11, 42] is a popular method for Bayesian inference involving an intractable, or expensive to evaluate, likelihood function but where simulation from the model is easy. The method consists of defining an alternative likelihood function which is also in general intractable but naturally lends itself to pseudo-marginal computations [5], hence making the approach of practical interest. The aim of this chapter is to show the connections of ABC Markov chain Monte Carlo with pseudo-marginal algorithms, review their existing theoretical results, and discuss how these can inform practice and hopefully lead to fruitful methodological developments. peerReviewed
Sattumaa satumaassa : todennäköisyyslaskentaa nopanheitosta mittateoriaan
2015
Hölder regularity for stochastic processes with bounded and measurable increments
2022
We obtain an asymptotic Hölder estimate for expectations of a quite general class of discrete stochastic processes. Such expectations can also be described as solutions to a dynamic programming principle or as solutions to discretized PDEs. The result, which is also generalized to functions satisfying Pucci-type inequalities for discrete extremal operators, is a counterpart to the Krylov-Safonov regularity result in PDEs. However, the discrete step size $\varepsilon$ has some crucial effects compared to the PDE setting. The proof combines analytic and probabilistic arguments.