Estimation of the transition matrix in Markov Chain model of customer lifetime value using flower pollination algorithm

2015 ◽  
Vol 9 ◽  
pp. 3409-3418 ◽  
Author(s):  
Udjianna S. Pasaribu ◽  
Fathimah al-Ma’shumah ◽  
Dony Permana
Keyword(s):  
Markov Chain ◽  
Transition Matrix ◽  
Markov Chain Model ◽  
Customer Lifetime Value ◽  
Flower Pollination Algorithm ◽  
Chain Model ◽  
Flower Pollination ◽  
Customer Lifetime ◽  
Lifetime Value

MODEL STOKASTIK RANTAI MARKOV EMPAT STATUS PADA PENENTUAN NILAI HIDUP PELANGGAN

2017 ◽  
Vol 18 (01) ◽  
pp. 78-85
Author(s):  
Dony Permana
Keyword(s):  
Markov Chain ◽  
Interest Rates ◽  
Transition Matrix ◽  
State Transition ◽  
Retention Rate ◽  
Product Price ◽  
Acquisition Rate ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Probability Transition Matrix

Customer Lifetime Value, familiar as CLV is valuability a customer in marketing system. High CLV has a meaning that the customer will bring in a big return for a firm. CLV is determined by some factors, as retention rate, acquisition rate, some costs, product price, and interest rates. Markov Chain is one of model that used to determine CLV.  In Markov Chain, a customer is assumed some state. Transition inter states are assumed Markovian. Here, we make CLV model using Markov Chain with four states. There are four type of model that have four states. Each type have different transition chart and of course have different probability transition matrix. Here, we describe every type of CLV model using Markov Chain.

scholarly journals Overdominant Alleles in a Population of Variable Size

Genetics ◽  
1999 ◽  
Vol 152 (2) ◽  
pp. 775-781 ◽  
Author(s):  
Montgomery Slatkin ◽  
Christina A Muirhead
Keyword(s):  
Markov Chain ◽  
Approximate Method ◽  
Population Size ◽  
Transition Matrix ◽  
Markov Chain Model ◽  
Population Bottleneck ◽  
The Other ◽  
Chain Model ◽  
Variable Size ◽  
Constant Size

Abstract An approximate method is developed to predict the number of strongly overdominant alleles in a population of which the size varies with time. The approximation relies on the strong-selection weak-mutation (SSWM) method introduced by J. H. Gillespie and leads to a Markov chain model that describes the number of common alleles in the population. The parameters of the transition matrix of the Markov chain depend in a simple way on the population size. For a population of constant size, the Markov chain leads to results that are nearly the same as those of N. Takahata. The Markov chain allows the prediction of the numbers of common alleles during and after a population bottleneck and the numbers of alleles surviving from before a bottleneck. This method is also adapted to modeling the case in which there are two classes of alleles, with one class causing a reduction in fitness relative to the other class. Very slight selection against one class can strongly affect the relative frequencies of the two classes and the relative ages of alleles in each class.

scholarly journals Numerical Study of Turbid Slab Optical Properties Reconstruction from Multiple Scattering Signals Using Time-Based Markov Chain Model

2021 ◽  
Vol 11 (2) ◽  
pp. 588
Author(s):  
Hujie Pan ◽  
Qinglin Xu ◽  
Xuesong Li ◽  
Shangning Wang ◽  
Min Xu
Keyword(s):  
Optical Properties ◽  
Markov Chain ◽  
Multiple Scattering ◽  
Transition Matrix ◽  
Numerical Study ◽  
Markov Chain Model ◽  
Chain Model ◽  
Layer By Layer ◽  
Reconstruction Process ◽  
Computational Costs

The reconstruction of optical properties for opaque mediums is highly desired for medical, atmosphere and aerosol applications. However, the modeling and reconstruction process is highly related with multiple scattering phenomena, which elevates both the complexity and computational costs for such efforts. This work introduces a time-based Markov chain method, which uses a sparse transition matrix to represent the likelihood for a photon to transit in the turbid media. The accuracy of the time-based Markov chain model was verified against the forwarding calculations of the scattering-based Markov chain model and Monte Carlo simulations. Then, reconstruction was performed with backscattered photon angular distributions. Based on the characteristics of the sparse transition matrix, the optical properties (droplet diameters) could be obtained layer by layer with transmitted photon distributions at different time durations. It is shown that the time-based Markov chain model can reconstruct the optical properties of a turbid slab with satisfactory accuracy and lower computational costs.

scholarly journals Classification of customer lifetime value models using Markov chain

2017 ◽  
Vol 893 ◽  
pp. 012026
Author(s):  
Dony Permana ◽  
Udjianna S. Pasaribu ◽  
Sapto W. Indratno ◽  
Suprayogi
Keyword(s):  
Markov Chain ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value
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