Results 151 to 160 of about 105,246 (202)

Network motif detection using hidden markov models. [PDF]

Sci Rep
Bampos C, Megalooikonomou V.
europepmc   +1 more source

Voltage-controlled magnetoelectric devices for neuromorphic diffusion process. [PDF]

Nat Commun
Cheng Y, Shu Q, Lee A, He H, Zhu I, Chen M, Chen R, Wang Z, Zhang H, Wang CY, Yang SY, Hsin YC, Shih CY, Lee HH, Cheng R, Wang KL.   +15 more
europepmc   +1 more source

Cost-effectiveness and budget impact analysis of switching from apixaban to rivaroxaban treatment among patients with nonvalvular atrial fibrillation in a German healthcare setting. [PDF]

J Comp Eff Res
Subash R, Strakosch T, Zhang M, Hagan M, Dworatzek E, Kisser A, Vasilopoulos V, Salter C, Dickerson C, Stawowczyk E.   +9 more
europepmc   +1 more source

Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition. [PDF]

BMC Bioinformatics
Yousefian M, Frank AS, Weber M, Röblitz S.   +3 more
europepmc   +1 more source

Research on online EDI order scheduling optimization strategy in manufacturing enterprises based on time-varying Markov chains. [PDF]

Sci Rep
Wulan Q.
europepmc   +1 more source

Noise-induced bimodality in self-regulated gene networks with nonlinear promoter transitions and fast dimerization. [PDF]

J Math Biol
Hung SH, Tsai JC, Wu CC.
europepmc   +1 more source

Vasopressin and angiotensin II pathways differentially modulate human fear response dynamics to looming threats. [PDF]

PLoS Biol
Han M, Dong W, Fu K, Wang J, Xu Y, Zheng Y, Kendrick K, Ferraro S, Xu T, Yao D, Becker B.   +10 more
europepmc   +1 more source

Response: Commentary: Outlining a novel psychometric model of mental flexibility and affect dynamics. [PDF]

Front Psychol
Borghesi F, Chirico A, Cipresso P.
europepmc   +1 more source

Changes in waterfowl movement behavior in response to hunting pressure. [PDF]

Mov Ecol
Beatty KE, Huck NR, Buderman FE.
europepmc   +1 more source

Moments of Markov switching models [PDF]

Journal of Econometrics, 2000
Let \(\{\varepsilon_t\}\) be i.i.d. \(N(0,1)\) random variables and \(S_t\) an unobserved stationary ergodic \(k\)-state Markov homogeneous process. The author deals with three types of Markov switching models, namely (MS I) \(y_t=\mu_{S_t} +\sigma_{S_t}\varepsilon_t\), (MS II) \(y_t=\mu_{S_t} +\varphi_1(y_{t-1}-\mu_{S_{t-1}})+\sigma_{S_t}\varepsilon_t\
openaire   +1 more source