The page numbers indicated between parentheses correspond to the printed version
page 43 (44):
expresion 2.1 should be
or equivalently
page 50 (51):
- As we can see, the result looks somewhat similar for lossf_a is
$\hat \theta = 0.32$
page 53 (54):
- As I am not a [not a] ethologist [...]
page 59 (60):
- second paragraph: "I(t')s the theoretical distribution [...]"
- last paragraph, third sentence: replace "for
$\nu > 2$ " with "for$\nu <= 2$ "
page 63 (64):
- second paragraph: "how a few of the predictive samples look[s] very flat."
page 64 (65):
- first paragraph: "to the value[s] estimated"
page 65 (66):
- second paragraph: "help their kids grow[n] stronger"
page 66 (67):
- the pooled standard deviation should have a plus (+) instead of a minus (-) sign between the group standard deviations. That is:
$$\frac{\mu_2 - \mu_1}{\sqrt{\frac{{\sigma_2}^2 + {\sigma_1}^2}{2}}}$$
page 73 (74):
- first paragraph: "i(t')s also possible"
page 74 (75):
- first bullet point: "We have [have] defined two [...]"
page 83 (84):
- first paragraph: "And that is, ladies, gentlem[a] (e) n"
page 95 (97):
- first paragraph: "this constrain(t) is relaxed"
page 100 (102):
- second to last paragraph: "interval [-1, 1][. It does not matter about](, regardless of) the scale of the data."
- last paragraph: "how much y change(s)"
page 109 (111):
- code block, 4th and 3rd line from bottom: f-strings missing the "f".
page 116 (118):
- first paragraph: "with an[d] increasing amount"
page 119 (121):
- second paragraph: "They are just (k)nobs"
page 120 (122):
- first paragraph: "Well that's the subject of Chapter [6] (5), Model Comparison"
page 121 (123):
Here,
page 128 (130):
-
first paragraph: "Using a fo(rest plot)"
-
Figure 3.22 have been updated
page 133 (135):
- Figure 3.26 have been updated
page 135 (137):
- In all of the examples we have seen so far, the (in)dependent variables contribute additively to the predicted variable.
page 136 (138):
- For those cases, we may want to consider the variance as a (linear) function of the (in)dependent variable.
Page 141 (143):
- Exercise 6: "ArviZ functions like plot_trace and plot_pair"
Page 142 (144):
- Exercise 14: This time (a)dd uncertainy to the linear plot
- Exercise 14: Exercise should reference Figure 3.17, not Figure 3.18
page 154 (156):
- second paragraph: "we take advantage[s]"
page 160 (162):
- third paragraph: "and 50 virgini[n] (c) as"
page 163 (165):
- bottom paragraph: "Chapter 5, [Modeling with Linear Regression] (Model Comparison)"
page 167 (169):
- last line: "$x!$ is the factorial of
$x$ , that is,$x! = x \times (x-1) \times (x-2) \times \dots \times 2 \times 1$ .
page 170 (172):
- equation (4.25):
$$p(y_j = k_i) = \psi \frac{\mu^{x_i}e^{-\mu}}{x_i!}$$
page 175 (177):
- "Extensions such (as) the ones we [we] saw"
page 185 (187):
- first paragraph: "shocked or even disappoint[ing] (ed) by"
- equation 5.1:
$p( T_{sim} > T_{obs} | y)$
Page 216 (218):
- "KL diverge(nce) is useful because it is a way of measuring how close to distributins are
page 219 (221):
- Bayes factors are problematic to use, given that they are very sensitive[ly] to prior specification,
page 230 (232):
- last paragraph: "does not necessary depend(s) on data"
page 237 (239):
- third paragraph: "what we do to turn logistic[s] regression into a"
page 238 (240):
- first paragraph: "someone already decide(d) the name"
page 239 (241):
- third paragraph: "br[ake] (eak) the stick"
page 241 (243):
- first paragraph: "represent(s) how confiden[ce] (t) we are"
page 245 (247):
- first paragraph: "This model also show(s) a less smooth"
page 247 (249):
- last paragraph: "the latent variable before doing inference, wi[n]ch may lead to"
page 248 (250):
-
second paragraph:
- "thus i[n] (t) may be convenient"
- "th[is] (ese) models can lead"
-
last paragraph: "can be interpreted as continuous mixture model(s)"
page 253 (255):
- last paragraph:
- "we express the first [one] function"
- "for the second [one] function"
page 255 (258):
- equation 7.4: second term on the RHS should be:
$(x_2 - x_2')^2$ - third paragraph: "covariance matrix looks [appears] for different inputs"
- info box: "Thus, the close[st] (r) two points are on the x axis[;] (,) the mo[st] (re) similar we expect their values to be on the y axis"
page 259 (262):
- last paragraph: "and [this is not the exception with] Gaussian processes (are no exception)"
page 261 (264):
- second paragraph: "$x$ is the independent variable[s], and
$y$ is the dependent variable[s]" - third paragraph: "module, [Often, ]for length-scale parameters, priors avoiding zero (often) work better"
page 267 (270):
- first paragraph: "their geographical similar[ly] (ity)"
page 271 (274):
- second paragraph: "counteracting the effect of it('s)[ over] close neighbors"
page 279 (282):
- last paragraph: "to the time a disaster[s] happen[s] (ed)"
page 280 (283):
- first paragraph: "Let's load [at] the data"
page 285 (288):
- last paragraph: "We may imag[e] (ine) that"
page 295 (298):
- second paragraph: "[Also is] (It's also) one of the building block(s)"
page 303 (306):
- third paragraph: "The rule to decide whether to accept or reject is known as the Metropolis criteri[a] (on)"
page 304 (307):
-
step 2, it should read Choose a new parameter value
$x_{i+1}$ rather than Choose a new parameter value$x_i+1$ . -
The uppercase Q's should be lowercase q's
$q(x_{i+1} \mid x_i)$
page 318 (321):
- second paragraph: "samples from the noncentered model ha[s] (ve) almost no autocorrelation"
page 319 (322):
- second paragraph: " we will need a [more] (larger) effective sample size"
page 327 (331):
- first paragraph:
- "One book that is generally refere[e]d (to) as"
- "You may also want to check (out) the book"