<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Song, Kevin</style></author><author><style face="normal" font="default" size="100%">Park, Raymond</style></author><author><style face="normal" font="default" size="100%">Das, Atanu</style></author><author><style face="normal" font="default" size="100%">Makarov, Dmitrii E.</style></author><author><style face="normal" font="default" size="100%">Vouga, Etienne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-Markov models of single-molecule dynamics from information-theoretical analysis of trajectories</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">159</style></volume><pages><style face="normal" font="default" size="100%">064104</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Whether single-molecule trajectories, observed experimentally or in molecular simulations, can be described using simple models such as biased diffusion is a subject of considerable debate. Memory effects and anomalous diffusion have been reported in a number of studies, but directly inferring such effects from trajectories, especially given limited temporal and/or spatial resolution, has been a challenge. Recently, we proposed that this can be achieved with information-theoretical analysis of trajectories, which is based on the general observation that non-Markov effects make trajectories more predictable and, thus, more ``compressible'' by lossless compression algorithms. Toy models where discrete molecular states evolve in time were shown to be amenable to such analysis, but its application to continuous trajectories presents a challenge: the trajectories need to be digitized first, and digitization itself introduces non-Markov effects that depend on the specifics of how trajectories are sampled. Here we develop a milestoning-based method for information-theoretical analysis of continuous trajectories and show its utility in application to Markov and non-Markov models and to trajectories obtained from molecular simulations.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	4.4&lt;/p&gt;
</style></custom4></record></records></xml>