Alós et al., 2016 - Google Patents
Bayesian state-space modelling of conventional acoustic tracking provides accurate descriptors of home range behavior in a small-bodied coastal fish speciesAlós et al., 2016
View HTML- Document ID
- 6971954972573740743
- Author
- Alós J
- Palmer M
- Balle S
- Arlinghaus R
- Publication year
- Publication venue
- PloS one
External Links
Snippet
State-space models (SSM) are increasingly applied in studies involving biotelemetry- generated positional data because they are able to estimate movement parameters from positions that are unobserved or have been observed with non-negligible observational …
- 241000251468 Actinopterygii 0 title abstract description 84
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Alós et al. | Bayesian state-space modelling of conventional acoustic tracking provides accurate descriptors of home range behavior in a small-bodied coastal fish species | |
| Silva et al. | Using dynamic Brownian Bridge Movement Models to identify home range size and movement patterns in king cobras | |
| Bestley et al. | Taking animal tracking to new depths: synthesizing horizontal–vertical movement relationships for four marine predators | |
| Krause et al. | Reality mining of animal social systems | |
| Avgar et al. | Space‐use behaviour of woodland caribou based on a cognitive movement model | |
| Le Guen et al. | Reproductive performance and diving behaviour share a common sea‐ice concentration optimum in Adélie penguins (Pygoscelis adeliae) | |
| Soanes et al. | How many seabirds do we need to track to define home‐range area? | |
| Papastamatiou et al. | Telemetry and random‐walk models reveal complex patterns of partial migration in a large marine predator | |
| Pirotta et al. | Predicting the effects of human developments on individual dolphins to understand potential long-term population consequences | |
| McClintock | Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation | |
| Louzao et al. | Conserving pelagic habitats: seascape modelling of an oceanic top predator | |
| Bird et al. | Estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture–recapture data | |
| Breed et al. | Predicting animal home‐range structure and transitions using a multistate Ornstein‐Uhlenbeck biased random walk | |
| Isojunno et al. | Individual, ecological, and anthropogenic influences on activity budgets of long‐finned pilot whales | |
| Chimienti et al. | Movement patterns of large juvenile loggerhead turtles in the Mediterranean Sea: Ontogenetic space use in a small ocean basin | |
| Williamson et al. | Analysing detection gaps in acoustic telemetry data to infer differential movement patterns in fish | |
| Fahlbusch et al. | Blue whales increase feeding rates at fine-scale ocean features | |
| Le Bras et al. | How elephant seals (Mirounga leonina) adjust their fine scale horizontal movement and diving behaviour in relation to prey encounter rate | |
| Warwick-Evans et al. | Predictive modelling to identify near-shore, fine-scale seabird distributions during the breeding season | |
| Mikkelsen et al. | Comparing distribution of harbour porpoises (Phocoena phocoena) derived from satellite telemetry and passive acoustic monitoring | |
| Winton et al. | A spatial point process model to estimate individual centres of activity from passive acoustic telemetry data | |
| Phillips et al. | Objective classification of latent behavioral states in bio‐logging data using multivariate‐normal hidden Markov models | |
| Nordstrom et al. | Jellyfish distribution in space and time predicts leatherback sea turtle hot spots in the Northwest Atlantic | |
| Pirotta et al. | Modeling the functional link between movement, feeding activity, and condition in a marine predator | |
| Brown et al. | Acceleration as a proxy for energy expenditure in a facultative‐soaring bird: comparing dynamic body acceleration and time‐energy budgets to heart rate |