Message-ID: <CAEsSYzxi=bGhr8TUNBTPqo0ZEwAefH-5EBP7a+GVW5B0TkAKbg@mail.gmail.com>
Date: 2024-06-24T18:11:46Z
From: Oliver Hooker
Subject: Hidden Markov Models for Movement, Acceleration and other Ecological Data – an introduction using moveHMM and momentuHMM in R (HMMM01)
ONLINE COURSE ? Hidden Markov Models for Movement, Acceleration and other
Ecological Data ? an introduction using moveHMM and momentuHMM in R (HMMM01)
https://www.prstats.org/course/hidden-markov-models-for-movement-acceleration-and-other-ecological-data-hmmm01/
18th - 21st September 2024
Please feel free to share.
Instructor - Dr. Roland Langrock
ABOUT THIS COURSE; Hidden Markov models (HMMs) are flexible statistical
models for time series observations driven byunderlying states. Over the
last decade, HMMs have become increasingly popular within the ecological
community as they allow to uncover behavioural state dynamics from noisy
sensor data. For example, a typical HMM-based analysis of say GPS locations
or acceleration measurements could involve the investigation of internal
(e.g. sex, size, age) and external (e.g. temperature, habitat) drivers of
behavioural state occupancy.
This workshop will introduce the HMM framework, comprising a mix of
theoretical lectures and hands-on practical components using R. In the
theoretical sessions, the following topics will be covered:
? motivation & overview
? basic HMM formulation
? fitting an HMM to data
? model selection & model checking
? state decoding
? incorporating covariates, seasonality and random effects
? other model extensions
These techniques will be illustrated primarily using movement and
acceleration data, but are applicable also to other ecological time series
data (e.g. capture-recapture). In the practical sessions, we will focus on
HMM analyses using the R packages moveHMM and momentuHMM, but will also
showcase the use of hmmTMB. Basic knowledge of the free software R is
helpful, but not required.
A basic understanding of statistics and probability calculus, as it would
be taught in any introductory statistics class, is required. By the end of
the course, participants will have a good understanding of what HMMs are
and what they can be used for. Participants will also be prepared to tailor
a suitable HMM to their data and to implement the corresponding analysis in
R.
Please email oliverhooker at prstatistics.com with any questions
--
Oliver Hooker PhD.
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