0000000000414167

AUTHOR

Heikki Haario

showing 4 related works from this author

Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing

2004

SummaryWe discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the ‘Global ozone monitoring of occultation of stars’ instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for…

Statistics and Probability010504 meteorology & atmospheric sciencesAttenuationInversion (meteorology)Markov chain Monte CarloDensity estimationInverse problem01 natural sciencesOccultation010104 statistics & probabilitysymbols.namesakeMetropolis–Hastings algorithmStatisticsPrior probabilitysymbols0101 mathematicsStatistics Probability and UncertaintyAlgorithm0105 earth and related environmental sciencesMathematicsJournal of the Royal Statistical Society Series B: Statistical Methodology
researchProduct

Componentwise adaptation for high dimensional MCMC

2005

We introduce a new adaptive MCMC algorithm, based on the traditional single component Metropolis-Hastings algorithm and on our earlier adaptive Metropolis algorithm (AM). In the new algorithm the adaption is performed component by component. The chain is no more Markovian, but it remains ergodic. The algorithm is demonstrated to work well in varying test cases up to 1000 dimensions.

Statistics and ProbabilityMathematical optimization010504 meteorology & atmospheric sciencesMonte Carlo methodMarkov processMarkov chain Monte Carlo01 natural sciencesStatistics::Computation010104 statistics & probabilityComputational Mathematicssymbols.namesakeMetropolis–Hastings algorithmTest caseChain (algebraic topology)Component (UML)symbolsStatistics::MethodologyErgodic theory0101 mathematicsStatistics Probability and Uncertainty0105 earth and related environmental sciencesMathematicsComputational Statistics
researchProduct

Characteristic asymptotics for fast chemical reaction

1995

010101 applied mathematicsGeneral Mathematics010102 general mathematicsMathematical analysisStatistical physics0101 mathematics01 natural sciencesChemical reactionMathematicsAsymptotic Analysis
researchProduct

Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter

2013

Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use adaptive MCMC algorithms which automatically tune the statistics of a proposal distribution during the MCMC run. A new adaptive MCMC algorithm, called the variational Bayesian adaptive Metropolis (VBAM) algorithm, is developed. The VBAM algorithm updates the proposal covariance matrix using the variational Bayesian adaptive Kalman filter (VB-AKF). A strong law of large numbers for the VBAM algorithm is…

Statistics and ProbabilityMathematical optimizationCovariance matrixApplied MathematicsBayesian probabilityRejection samplingMathematics - Statistics TheoryMarkov chain Monte CarloStatistics Theory (math.ST)Kalman filterStatistics::ComputationComputational Mathematicssymbols.namesakeComputingMethodologies_PATTERNRECOGNITIONMetropolis–Hastings algorithmComputational Theory and MathematicsConvergence (routing)FOS: MathematicsKernel adaptive filtersymbolsMathematicsComputational Statistics & Data Analysis
researchProduct