Results 1 to 10 of about 2,628 (112)

Non‐Markovian maximum likelihood estimation of autocorrelated movement processes [PDF]

Methods in Ecology and Evolution, 2014
SummaryBy viewing animal movement paths as realizations of a continuous stochastic process, we introduce a rigorous likelihood method for estimating the statistical parameters of movement processes. This method makes no assumption of a hidden Markov property, places no special emphasis on the sampling rate, is insensitive to irregular sampling and data
Fleming, Christen H., Calabrese, Justin M., Mueller, Thomas, Olson, Kirk A., Leimgruber, Peter, Fagan, William F.   +5 more
openaire   +2 more sources

Estimates for first exit times of non-Markovian Itô processes [PDF]

Stochastics, 2008
First exit times and their path-wise dependence on trajectories are studied for non-Markovian Ito processes. Estimates of distances between two exit times are obtained. In particular, it follows that first exit times of two Ito processes are close if their trajectories are close.
openaire   +2 more sources

Uncertainty-Aware Remaining Useful Life Prediction via Synergizing TCN-Transformer Networks and Fractional Brownian Motion. [PDF]

Entropy (Basel)
Geng Y, Yu T, Liu Y, Zhao J.
europepmc   +1 more source

Dynamic mechanism for subtype selectivity of endocannabinoids. [PDF]

J Biol Chem
Dutta S, Zhao L, Shukla D.
europepmc   +1 more source

Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling. [PDF]

Ecology
Perry RW, Pope AC, Hendrix AN, Kirsch JE, Matthias BG, Dodrill MJ.   +5 more
europepmc   +1 more source

PSMC-FAC: Automated Optimization of False-Negative Rate Corrections for Low-Coverage PSMC-Based Demographic Inference. [PDF]

Biology (Basel)
Iglesias-Santos F, Nieto A, Casillas S, Barbadilla A, Sarabia C.   +4 more
europepmc   +1 more source

Neural barcoding representing cortical spatiotemporal dynamics based on continuous-time Markov chains. [PDF]

Cell Rep Methods
Culp JM, Ashby DM, George AG, Teskey GC, Nicola W, McGirr A.   +5 more
europepmc   +1 more source

Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. [PDF]

PLoS One
Raherinirina A, Tsilefa ST, Nirilanto T, Manou-Abi SM.   +3 more
europepmc   +1 more source

The Challenge of Time-to-Event Analysis for Multiple Events: A Guided Tour From Time-to-First-Event to Recurrent Time-to-Event Analysis. [PDF]

Biom J
Schmeller S, Erdmann A, Beyersmann J, Angermann C, Ozga AK.   +4 more
europepmc   +1 more source

Network structure induced bias in estimates of intrinsic generation times. [PDF]

PLoS Comput Biol
Kollepara PK, Poletto C, Miller JC.
europepmc   +1 more source