SSMBook/_build/jupyter_execute/chapters/ssm/ssm_intro.py

#!/usr/bin/env python
# coding: utf-8

# (sec:ssm-intro)=
# # What are State Space Models?
# 
# 
# A state space model or SSM
# is a partially observed Markov model,
# in which the hidden state,  $\hidden_t$,
# evolves over time according to a Markov process,
# possibly conditional on external inputs or controls $\input_t$,
# and each hidden state generates some
# observations $\obs_t$ at each time step.
# (In this book, we mostly focus on discrete time systems,
# although  we consider the continuous-time case in  XXX.)
# We get to see the observations, but not the hidden state.
# Our main goal is to infer the hidden state given the observations.
# However, we can also use the model to predict future observations,
# by first predicting future hidden states, and then predicting
# what observations they might generate.
# By using  a hidden state $\hidden_t$
# to represent the past observations, $\obs_{1:t-1}$,
# the  model can have ``infinite'' memory,
# unlike a standard Markov model.
# 
# ```{figure} /figures/SSM-AR-inputs.png
# :height: 150px
# :name: fig:ssm-ar
# 
# Illustration of an SSM as a graphical model.
# ```
# 
# 
# Formally we can define an SSM 
# as the following joint distribution:
# ```{math}
# :label: eq:SSM-ar
# p(\obs_{1:T},\hidden_{1:T}|\inputs_{1:T})
#  = \left[ p(\hidden_1|\inputs_1) \prod_{t=2}^{T}
#  p(\hidden_t|\hidden_{t-1},\inputs_t) \right]
#  \left[ \prod_{t=1}^T p(\obs_t|\hidden_t, \inputs_t, \obs_{t-1}) \right]
# ```
# where $p(\hidden_t|\hidden_{t-1},\inputs_t)$ is the
# transition model,
# $p(\obs_t|\hidden_t, \inputs_t, \obs_{t-1})$ is the
# observation model,
# and $\inputs_{t}$ is an optional input or action.
# See {numref}`fig:ssm-ar` 
# for an illustration of the corresponding graphical model.
# 
# 
# We often consider a simpler setting in which the
#  observations are conditionally independent of each other
# (rather than having Markovian dependencies) given the hidden state.
# In this case the joint simplifies to 
# ```{math}
# :label: eq:SSM-input
# p(\obs_{1:T},\hidden_{1:T}|\inputs_{1:T})
#  = \left[ p(\hidden_1|\inputs_1) \prod_{t=2}^{T}
#  p(\hidden_t|\hidden_{t-1},\inputs_t) \right]
#  \left[ \prod_{t=1}^T p(\obs_t|\hidden_t, \inputs_t) \right]
# ```
# Sometimes there are no external inputs, so the model further
# simplifies to the following unconditional generative model: 
# ```{math}
# :label: eq:SSM-no-input
# p(\obs_{1:T},\hidden_{1:T})
#  = \left[ p(\hidden_1) \prod_{t=2}^{T}
#  p(\hidden_t|\hidden_{t-1}) \right]
#  \left[ \prod_{t=1}^T p(\obs_t|\hidden_t) \right]
# ```
# See {numref}`ssm-simplified` 
# for an illustration of the corresponding graphical model.
# 
# 
# ```{figure} /figures/SSM-simplified.png
# :height: 150px
# :name: ssm-simplified
# 
# Illustration of a simplified SSM.
# ```
# 
# SSMs are widely used in many areas of science, engineering, finance, economics, etc.
# The main applications are state estimation (i.e., inferring the underlying hidden state of the system given the observation),
# forecasting (i.e., predicting future states and observations), and control (i.e., inferring the sequence of inputs that will
# give rise to a desired target state). We will discuss these applications in later chapters.

#