MAINT: Fix small typos

lectures/aiyagari.md

@@ -55,7 +55,6 @@ The Aiyagari model has been used to investigate many topics, including
 Let's start with some imports:
 
 ```{code-cell} ipython
-%matplotlib inline
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np

lectures/ar1_bayes.md

@@ -34,8 +34,6 @@ import jax.numpy as jnp
 from jax import random
 import matplotlib.pyplot as plt
 
-%matplotlib inline
-
 import logging
 logging.basicConfig()
 logger = logging.getLogger('pymc')
@@ -71,7 +69,7 @@ $$ (eq:themodel_2)
 
 
 
-Consider a sample $\{y_t\}_{t=0}^T$ governed by this statistical model.  
+Consider a sample $\{y_t\}_{t=0}^T$ governed by this statistical model.
 
 The model
 implies that the likelihood function of $\{y_t\}_{t=0}^T$ can be **factored**:
@@ -80,7 +78,7 @@ $$
 f(y_T, y_{T-1}, \ldots, y_0) = f(y_T| y_{T-1}) f(y_{T-1}| y_{T-2}) \cdots f(y_1 | y_0 ) f(y_0)
 $$
 
-where we use $f$ to denote a generic probability density.  
+where we use $f$ to denote a generic probability density.
 
 The  statistical model {eq}`eq:themodel`-{eq}`eq:themodel_2` implies
 
@@ -95,7 +93,7 @@ We want to study how inferences about the unknown parameters $(\rho, \sigma_x)$
 
 Below, we study two widely used alternative assumptions:
 
--  $(\mu_0,\sigma_0) = (y_0, 0)$ which means  that $y_0$ is  drawn from the distribution ${\mathcal N}(y_0, 0)$; in effect, we are **conditioning on an observed initial value**.  
+-  $(\mu_0,\sigma_0) = (y_0, 0)$ which means  that $y_0$ is  drawn from the distribution ${\mathcal N}(y_0, 0)$; in effect, we are **conditioning on an observed initial value**.
 
 -  $\mu_0,\sigma_0$ are functions of $\rho, \sigma_x$ because $y_0$ is drawn from the stationary distribution implied by $\rho, \sigma_x$.
 
@@ -105,20 +103,20 @@ Below, we study two widely used alternative assumptions:
 
 Unknown parameters are $\rho, \sigma_x$.
 
-We have  independent **prior probability distributions** for $\rho, \sigma_x$ and want to compute a posterior probability distribution after observing a sample $\{y_{t}\}_{t=0}^T$.  
+We have  independent **prior probability distributions** for $\rho, \sigma_x$ and want to compute a posterior probability distribution after observing a sample $\{y_{t}\}_{t=0}^T$.
 
 The notebook uses `pymc4` and `numpyro` to compute a posterior distribution of $\rho, \sigma_x$. We will use NUTS samplers to generate samples from the posterior in a chain. Both of these libraries support NUTS samplers.
 
 NUTS is a form of Monte Carlo Markov Chain (MCMC) algorithm that bypasses random walk behaviour and allows for convergence to a target distribution more quickly. This not only has the advantage of speed, but allows for complex models to be fitted without having to employ specialised knowledge regarding the theory underlying those fitting methods.
 
 Thus, we explore consequences of making these alternative assumptions about the distribution of $y_0$:
 
-- A first procedure is to condition on whatever value of $y_0$ is observed. This amounts to assuming that the probability distribution of the random variable  $y_0$ is a Dirac delta function that puts probability one on the observed value of $y_0$.    
+- A first procedure is to condition on whatever value of $y_0$ is observed. This amounts to assuming that the probability distribution of the random variable  $y_0$ is a Dirac delta function that puts probability one on the observed value of $y_0$.
 
 - A second procedure  assumes that $y_0$ is drawn from the stationary distribution of a process described by {eq}`eq:themodel`
 so that  $y_0 \sim {\cal N} \left(0, {\sigma_x^2\over (1-\rho)^2} \right) $
 
-When the initial value $y_0$ is far out in a tail of the stationary distribution, conditioning on an initial value gives a posterior that is **more accurate** in a sense that we'll explain.   
+When the initial value $y_0$ is far out in a tail of the stationary distribution, conditioning on an initial value gives a posterior that is **more accurate** in a sense that we'll explain.
 
 Basically, when $y_0$ happens to be  in a tail of the stationary distribution and we **don't condition on $y_0$**, the likelihood function for $\{y_t\}_{t=0}^T$ adjusts the posterior distribution of the parameter pair $\rho, \sigma_x $ to make the observed value of $y_0$  more likely than it really is under the stationary distribution, thereby adversely twisting the posterior in short samples.
 
@@ -129,7 +127,7 @@ We begin by solving a **direct problem** that simulates an AR(1) process.
 
 How we select the initial value $y_0$ matters.
 
-   * If we think $y_0$ is drawn from the stationary distribution ${\mathcal N}(0, \frac{\sigma_x^{2}}{1-\rho^2})$, then it is a good idea to use this distribution as $f(y_0)$.  Why? Because $y_0$ contains information about $\rho, \sigma_x$.  
+   * If we think $y_0$ is drawn from the stationary distribution ${\mathcal N}(0, \frac{\sigma_x^{2}}{1-\rho^2})$, then it is a good idea to use this distribution as $f(y_0)$.  Why? Because $y_0$ contains information about $\rho, \sigma_x$.
 
    * If we suspect that $y_0$ is far in the tails of the stationary distribution -- so that variation in early observations in the sample have a significant **transient component** -- it is better to condition on $y_0$ by setting $f(y_0) = 1$.
 
@@ -273,7 +271,7 @@ summary_y0
 
 Please note how the posterior for $\rho$ has shifted to the right relative to when we conditioned on $y_0$ instead of assuming that $y_0$ is drawn from the stationary distribution.
 
-Think about why this happens.  
+Think about why this happens.
 
 ```{hint}
 It is connected to how Bayes Law (conditional probability) solves an **inverse problem** by putting high probability on parameter values

lectures/ar1_processes.md

@@ -47,7 +47,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import numpy as np
-%matplotlib inline
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 ```