Image fusion using the expectation-maximization algorithm and a hidden Markov model

2005 ◽  
Author(s):  
Jinzhong Yang ◽  
R.S. Blum
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Image Fusion ◽  
Expectation Maximization ◽  
Hidden Markov ◽  
Expectation Maximization Algorithm

Point and Figure Portfolio Optimization using Hidden Markov Models and Its Application on the Bumi Resources Tbk Shares

2021 ◽  
Vol 3 (1) ◽  
pp. 44-52
Author(s):  
Kastolan Kastolan ◽  
Berlian Setiawaty ◽  
N. K. Kutha Ardana
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Portfolio Optimization ◽  
Expectation Maximization ◽  
Stock Prices ◽  
Stock Price ◽  
Hidden Markov ◽  
Expectation Maximization Algorithm ◽  
Percentage Error ◽  
Time Sampling

AbstractThe problem of portfolio optimization is to select a trading strategy which maximizes the expected terminal wealth. Since the stocks are traded at discrete random times in a real-world market, we are interested in a time sampling method. The sampling of stock price is obtained from the process of time sampling which is used in a point and figure chart. Point and figure (PF) chart displays the up and down movements of unbalanced stock prices. The basic idea is to describe essential movements of the unbalanced stock prices using a hidden Markov model. The model parameters are transition probability matrices. They are estimated using maximum likelihood method and expectation maximization algorithm. The estimation procedure involves change of measure. The model is then applied to the stock price of Bumi Resources Tbk. collected on a daily basis. The estimated parameters are used to calculate the optimal portfolio using a recursive algorithm. The results show that the discrete hidden Markov model can be applied to describe essential movements of the stock price. The best result gives 93.63% accuracy of the estimate of observation sequence with mean absolute percentage error (MAPE) 3.63%. The numerical calculation shows that the optimal logarithmic PF-portfolio increases the wealth.Keywords: point and figure portfolio; optimization portfolio; discrete hidden Markov model; expectation maximization algorithm; stock price of Bumi Resources Tbk. AbstrakMasalah pengoptimalan portofolio adalah pemilihan strategi perdagangan yang dapat memaksimalkan kekayaan terminal yang diharapkan. Karena di pasar dunia nyata, saham diperdagangkan pada waktu acak yang berbeda, sehingga kami tertarik pada metode pengambilan sampel waktu. Proses pengambilan sampel waktu diperoleh sampling harga saham yang digunakan dalam diagram point and figure (PF-chart). Grafik point and figure hanya menampilkan pergerakan naik atau turun harga saham yang tidak seimbang. Ide dasarnya adalah untuk mendeskripsikan pergerakan esensial dari harga saham yang tidak seimbang menggunakan model hidden Markov. Parameter dari model ini adalah matriks probabilitas transisi. Parameter diestimasi menggunakan metode maximum likelihood dan algoritma expectation maximization. Prosedur estimasi melibatkan perubahan ukuran. Model ini kemudian diaplikasikan pada harga saham Bumi Resources Tbk. dari tanggal 2 Januari 2007 sampai dengan 31 Januari 2011. Hasil estimasi parameter tersebut digunakan untuk menghitung portofolio optimal menggunakan algoritma rekursif. Hasil penelitian ini menunjukkan bahwa model hidden Markov diskrit dapat diterapkan untuk menggambarkan pergerakan esensial dari harga saham. Model terbaik memberikan akurasi 93.63% dari estimasi deretan observasi dengan mean absolute percentage error (MAPE) 3,63% dan 5 faktor penyebab kejadian. Perhitungan numerik menunjukkan bahwa logaritma portofolio-PF yang optimal dapat meningkatkan kekayaan.Kata kunci: portofolio point and figure; optimalisasi portofolio; model hidden Markov diskrit; algoritma expectation maximization; harga saham PT Bumi Resources.

Prediction of Facies Distribution in a Clastic Reservoir Using a Hidden Markov Model Combined With an Expectation-Maximization Algorithm

2016 ◽  
Author(s):  
Hwasoo Suk ◽  
Baehyun Min ◽  
Joe M. Kang ◽  
Cheolkyun Jeong
Keyword(s):  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Expectation Maximization ◽  
Hidden Markov ◽  
Transition Probability ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Facies Distribution ◽  
Clastic Reservoir

This study determines facies distribution in a clastic reservoir using a hidden Markov model combined with an Expectation-Maximization algorithm. Iterating expectation and maximization steps of the algorithm builds the hidden Markov model by tuning the model parameters including initial state distribution, state transition probability distribution, and observable symbol probability distribution. Optimized model parameters contribute to improving the predictability of facies distribution along the well trajectory using core and logging data.

scholarly journals Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

2019 ◽  
Vol 39 (3) ◽  
pp. 742-752 ◽  
Author(s):  
Zairan Li ◽  
Dan Wang ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
R. Simon Sherratt ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Image Fusion ◽  
Plantar Pressure ◽  
Hidden Markov

A multifocus image fusion method by using hidden Markov model

2013 ◽  
Vol 287 ◽  
pp. 63-72 ◽  
Author(s):  
Wei Wu ◽  
Xiaomin Yang ◽  
Yu Pang ◽  
Jian Peng ◽  
Gwanggil Jeon
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Image Fusion ◽  
Hidden Markov ◽  
Fusion Method ◽  
Image Fusion Method

AGGREGATE CLAIM ESTIMATION USING BIVARIATE HIDDEN MARKOV MODEL

2018 ◽  
Vol 49 (1) ◽  
pp. 189-215
Author(s):  
Zarina Nukeshtayeva Oflaz ◽  
Ceylan Yozgatligil ◽  
A. Sevtap Selcuk-Kestel
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Negative Binomial ◽  
Hidden Markov ◽  
Expectation Maximization Algorithm ◽  
Third Party ◽  
Aggregate Claim ◽  
State Dependent ◽  
Dependent Part ◽  
Finite State

AbstractIn this paper, we propose an approach for modeling claim dependence, with the assumption that the claim numbers and the aggregate claim amounts are mutually and serially dependent through an underlying hidden state and can be characterized by a hidden finite state Markov chain using bivariate Hidden Markov Model (BHMM). We construct three different BHMMs, namely Poisson–Normal HMM, Poisson–Gamma HMM, and Negative Binomial–Gamma HMM, stemming from the most commonly used distributions in insurance studies. Expectation Maximization algorithm is implemented and for the maximization of the state-dependent part of log-likelihood of BHMMs, the estimates are derived analytically. To illustrate the proposed model, motor third-party liability claims in Istanbul, Turkey, are employed in the frame of Poisson–Normal HMM under a different number of states. In addition, we derive the forecast distribution, calculate state predictions, and determine the most likely sequence of states. The results indicate that the dependence under indirect factors can be captured in terms of different states, namely low, medium, and high states.

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