Estimation for partially observed Markov processes

DOI10.1007/BF01581757zbMath0819.60062MaRDI QIDQ1892250

T. N. Kaseke, Mary E. Thompson

Publication date: 15 August 1995

Published in: Stochastic Hydrology and Hydraulics (Search for Journal in Brave)

zbMATH Keywords

Monte CarloEM algorithmmaximum likelihood estimationfilteringhidden Markov modelsforward-backward algorithmmartingale estimating functionNash cascade modelrainfall runoff modeling

Mathematics Subject Classification ID

Point estimation (62F10) Hydrology, hydrography, oceanography (86A05) Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (1)

Estimation for rainfall-runoff modeled as a partially observed Markov process

Cites Work

• Unnamed Item
• Maximum-likelihood estimation for hidden Markov models
• Inference in hidden Markov models. I: Local asymptotic normality in the stationary case.
• Sampling-Based Approaches to Calculating Marginal Densities
• Dynamic Generalized Linear Models and Bayesian Forecasting
• Estimation for aggregate models: The aggregate Markov chain
• The foundations of finite sample estimation in stochastic processes
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Filtering and Smoothing Via Estimating Functions
• An Optimum Property of Regular Maximum Likelihood Estimation
• Exponential Families of Stochastic Processes: A Unifying Semimartingale Approach

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