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Linear Additive Markov Processes

Ravi Kumar, Maithra Raghu, Tamás Sarlós, Andrew Tomkins
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
We introduce LAMP: the Linear Additive Markov Process.  ...  Transitions in LAMP may be influenced by states visited in the distant history of the process, but unlike higher-order Markov processes, LAMP retains an efficient parameterization.  ...  [`], the Generalized Linear Additive Markov Process glamp(w, P, f) evolves according to the following transition rule: Pr[Xt = xt | x0, . . . t i} , xt).  ... 
doi:10.1145/3038912.3052644 dblp:conf/www/0001RST17 fatcat:5gllvoqozrdapd5t4fqlevt3nm

Linear Additive Markov Processes [article]

Ravi Kumar, Maithra Raghu, Tamas Sarlos, Andrew Tomkins
2017 arXiv   pre-print
We introduce LAMP: the Linear Additive Markov Process.  ...  Transitions in LAMP may be influenced by states visited in the distant history of the process, but unlike higher-order Markov processes, LAMP retains an efficient parametrization.  ...  LAMP In this section we introduce the Linear Additive Markov Process (LAMP), and describe its properties. Background Let [n] = {1, 2, . . . , n}. Let X be a state space of size n.  ... 
arXiv:1704.01255v1 fatcat:bgwvzqi2ijguhgwp266djewho4

Hitting probabilities in a Markov additive process with linear movements and upward jumps: Applications to risk and queueing processes

Masakiyo Miyazawa
2004 The Annals of Applied Probability  
Motivated by a risk process with positive and negative premium rates, we consider a real-valued Markov additive process with finitely many background states.  ...  It is known that the hitting probabilities of this additive process at lower levels have a matrix exponential form.  ...  This dual process is obtained from the Markov additive process by time reversal and changing the sign of the additive component.  ... 
doi:10.1214/105051604000000206 fatcat:4eqjki7oeraupbh6dbs7qpl5a4

Generalized Linear Models, Generalized Additive Models and Neural Networks: Comparative Study in Medical Applications [chapter]

Ana Luisa Papoila, Cristina Rocha, Carlos Geraldes, Patricia Xufre
2013 Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications  
The objective of this study is to compare the performance of generalized linear models (GLMs) with binary response ( McCullagh and Nelder, Generalized Linear Models.  ...  Chapman and Hall, London, 1989), with the performance of generalized additive models (GAMs) with binary response (Hastie and Tibshirani, Generalized Additive Models.  ...  process.  ... 
doi:10.1007/978-3-642-34904-1_33 fatcat:iv47ei3gejertlgfdjmdnmfsty

Page 770 of Mathematical Reviews Vol. , Issue 81B [page]

1981 Mathematical Reviews  
Matheson [Management Sci. 18 (1971/72), 356-369; MR 45 # 1583] the results for additive Markov decision Bh Economics, operations research, programming, games ™ processes cannot be used directly.  ...  The relations between the two formulations have been studied in the literature for Markov decision processes with an additive objective function and nice results have been achieved which can be used for  ... 

Page 2839 of Mathematical Reviews Vol. , Issue 2000d [page]

2000 Mathematical Reviews  
The additive coalescent is the A-valued Markov process in which pairs of clusters of masses {x;,x;} merge into a cluster of mass x; + x; at rate x; + x,;.  ...  P. (5-SYD-SM; Sydney); Law, J.S. (5-SYD; Sydney) Modelling random linear nucleation and growth by a Markov chain. (English summary) J. Appl. Probab. 36 (1999), no. 1, 273-278.  ... 

Analysis of Discrete Markov Process by Probability Flow Graph
確率流れ線図による不連続マルコフ過程の解析

Kensuke HASEGAWA
1967 Transactions of the Society of Instrument and Control Engineers  
A discrete stationary Markov process can be described by constant coefficient difference equations and analyzed conveniently by the z-transformation in the form of a generating function which reprensents  ...  The author defines the probability flow graph for the Markov process distinguishing it from the conventional flow graphs. Fig. 5 Cut of branch and addition of absorbing node  ...  At first, the Markov process is represented in a signal flow graph. After that, the author presents a new concept in understanding the Markov process.  ... 
doi:10.9746/sicetr1965.3.26 fatcat:blnn2a3d65ag5li7pavfvnc4xe

A Kalman filter clock algorithm for use in the presence of flicker frequency modulation noise

J A Davis, C A Greenhall, P W Stacey
2005 Metrologia  
The FFM is modelled approximately by a linear combination of Markov noise processes. Each Markov process is included in the Kalman filter and contributes an additional component to its state vector.  ...  Both the validity of the model and the effectiveness of adding these additional components to the state vector are examined.  ...  The FFM is modelled as a linear combination of Markov noise processes. Each Markov process is included in the Kalman filter and contributes an additional component to the state vector.  ... 
doi:10.1088/0026-1394/42/1/001 fatcat:hxgpqbfh4rbglmvdisfhuayysm

Page 1982 of Mathematical Reviews Vol. , Issue 2003C [page]

2003 Mathematical Reviews  
Summary: “For an additive Markov process (Xn,Sn), n> 0, in which x, is a countable Markov chain, properties of compo- nents of factorization of the operator J — zA(s), where A(s) = (M{e-S!  ...  (PL-SITU-IM; Gliwice) Properties of operators associated with Markov additive processes. Il. (Ukrainian. Ukrainian summary) Teor. Imovir. Mat. Stat. No. 57 (1997), 1-9; translation in Theory Probab.  ... 

An Efficient Computational Alternative to 'Using Linear Programming to Design Oil Pollution Detection Schedules'

Sandal S. Hart, Lee E. Daniel, Thom J. Hodgson
1978 A I I E Transactions  
programming formulation for Markov decision processes.  ...  Optimal schedules for patrol fligh ts of surveillance aircraft were found using linear programming. In this paper the model has been reformulated as a discre te time semi-Markov process.  ... 
doi:10.1080/05695557808975182 fatcat:zvz45moemfe2zl4mwyhn2mhdfq

Random perturbation methods with applications in Science and Engineering

2004 Computers and Mathematics with Applications  
Discrete-time stationary processes. 1.2. Discrete-time Markov processes. 1.3. Continuous-time stationary processes. 1.4. Continuous-time Markov processes. 2.  ...  Discrete-time Markov processes. 2.3.3. Discrete-time stationary processes. 2.3.4. Continuous-time stationary processes. 2.4. Large deviation theorems. 2.4.1. Continuous-time Markov processes. 3.  ... 
doi:10.1016/s0898-1221(04)90109-5 fatcat:5x5skcf2ijfuvixp2vdakmhqsa

Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices [article]

Marcell T. Kurbucz, Péter Pósfay, Antal Jakovác
2022 arXiv   pre-print
Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets  ...  To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function.  ...  law for Markov processes.  ... 
arXiv:2201.09790v1 fatcat:6b2e66iv4rbrnlmn25amoohshy

Semi-supervised Gaussian process latent variable model with pairwise constraints

Xiumei Wang, Xinbo Gao, Yuan Yuan, Dacheng Tao, Jie Li
2010 Neurocomputing  
(RBF + linear) kernel 1 st order Markov (RBF + linear) kernel 1 st order Markov Linear kernel 2 nd order Markov RBF kernel [Wang et al, 2005] 25 Optimization = inference of latent variables  ...  [Bishop, 2006; Williams and Rasmussen, 1996] 9 Gaussian Process Regression Mean of predictive distribution Target function Sampled (x n , t n ) with additive noise ±2 std of predictive  ... 
doi:10.1016/j.neucom.2010.01.021 fatcat:p7aelt5rtjeqfdfddy7sygcase

Comments Regarding `On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes' [article]

Sandip Roy
2018 arXiv   pre-print
The identifiability analysis of a networked Markov chain model known as the influence model, as described in a recent contribution to Arxiv, is examined.  ...  In addition, some concerns about the formulation of the identifiability problem and the proposed estimation approach are noted.  ...  Introduction The influence model, which describes a class of networked discrete-state Markov chains with quasi-linear interactions [2, 3] , has proved useful for representing social processes in human  ... 
arXiv:1811.02171v1 fatcat:vps6ymjab5edfgnvl4vnncq4wq

Page 7042 of Mathematical Reviews Vol. , Issue 93m [page]

1993 Mathematical Reviews  
Summary (translated from the Russian): “We consider a class of Markov decision processes (MDP) with additional constraints.  ...  from Markov decision processes.  ... 
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