rebuild / more refs

Author: ericward-noaa

Lectures/Week 5/lec_10_hmm.Rmd

@@ -124,6 +124,12 @@ between multiple states (2+)
 -   momentuHMM: R package for analysis of telemetry data using
     generalized multivariate hidden Markov models of animal movement
 
+## HMM Examples (this class):
+
+-   Rand et al. 2024 [https://doi.org/10.1016/j.ecolmodel.2024.110800](https://doi.org/10.1016/j.ecolmodel.2024.110800)
+
+-   Hodgson et al. 2020 [https://doi.org/10.1016/j.biocon.2020.108685](https://doi.org/10.1016/j.biocon.2020.108685)
+
 ## Regimes
 
 Lots of non-HMM approaches for detecting regimes
@@ -136,6 +142,31 @@ Lots of non-HMM approaches for detecting regimes
     -   how do we do change points with regression?
         ${ Y }_{ t }=B{ X }_{ t }+{ \varepsilon  }_{ t }$
 
+## Regimes: Nile River 
+
+```{r}
+data("Nile")
+plot(Nile)
+```
+
+## Regimes: Nile River
+
+Breakpoint / segmented regression
+
+* time of breaks passed in as data (as can other covariates)
+* all variance = observation error
+
+State space model
+
+* time of breaks + other covariates can be passed in or not
+* change is smooth
+* variance separated into observation, process error
+
+Hidden Markov Model
+
+* covariates can be passed in as data, time of breaks not
+* variance separated into observation, process
+
 ## Regimes: simulating data
 
 ```{r, fig.height=3}

Lectures/Week 5/lec_10_hmm.html

@@ -124,6 +124,12 @@ between multiple states (2+)
 -   momentuHMM: R package for analysis of telemetry data using
     generalized multivariate hidden Markov models of animal movement
 
+## HMM Examples (this class):
+
+-   Rand et al. 2024 [https://doi.org/10.1016/j.ecolmodel.2024.110800](https://doi.org/10.1016/j.ecolmodel.2024.110800)
+
+-   Hodgson et al. 2020 [https://doi.org/10.1016/j.biocon.2020.108685](https://doi.org/10.1016/j.biocon.2020.108685)
+
 ## Regimes
 
 Lots of non-HMM approaches for detecting regimes
@@ -136,6 +142,31 @@ Lots of non-HMM approaches for detecting regimes
     -   how do we do change points with regression?
         ${ Y }_{ t }=B{ X }_{ t }+{ \varepsilon  }_{ t }$
 
+## Regimes: Nile River 
+
+```{r}
+data("Nile")
+plot(Nile)
+```
+
+## Regimes: Nile River
+
+Breakpoint / segmented regression
+
+* time of breaks passed in as data (as can other covariates)
+* all variance = observation error
+
+State space model
+
+* time of breaks + other covariates can be passed in or not
+* change is smooth
+* variance separated into observation, process error
+
+Hidden Markov Model
+
+* covariates can be passed in as data, time of breaks not
+* variance separated into observation, process
+
 ## Regimes: simulating data
 
 ```{r, fig.height=3}

docs/Lectures/Week 5/lec_10_hmm.Rmd

@@ -349,7 +349,7 @@ <h2>
 </center>
 <p><br></p>
 <center>
-<em>This site was last updated at 13:14 on 29 Apr 2025</em>
+<em>This site was last updated at 08:52 on 01 May 2025</em>
 </center>