| Page | Printed text | Correct text | Thanks |
|---|---|---|---|
| 13 | The binomial coefficient is typeset as |
it should be |
Chris Hansen |
| 62 | We will explore the value of over a grid of 200 points. | We will explore the value over a grid of 200 points. | dweights |
| 88 (exercise 4) | wrong short url | The link should point to https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#case-study-2-coal-mining-disasters | DrEntropy |
| 94 | ...the variance between observed and theoretical values should be the same for all groups. | ...the variance between observed and theoretical values should be unique for each group. | Kenji Oman |
| 104 | Figure 3.6 indicates a HalfNormal | Figure 3.6 should indicate a Gamma | Jacob Warren |
| 115 | We are going to usetemperature | We are going to use temperature | Kenji Oman |
| 115 | The noise term is 𝜖 | The noise term is 𝜎 | Parrenin Frédéric |
| 116 | We commit it because otherwise... | We omit it because otherwise... | Kenji Oman |
| 122 | The variance of the NegativeBinomial is 𝜇 + 𝜇²/𝛼 , so the larger the value of 𝛼 the larger the variance. | The variance of the NegativeBinomial is 𝜇 + 𝜇²/𝛼 , so the larger the value of 𝛼 the smaller the variance. | Tomás Capretto |
| 124 | (or data with a few bulk points) | (or data with only a few bulk points) | Kenji Oman |
| 133 | We have been using the linear motif to model the mean of a distribution and, in the previous section, we used it to model interactions. In statistics,... | We have been using the linear motif to model the mean of a distribution. In statistics,... | Jacob Warren |
| 145 | In the next chapter, we will learn more about linear regression... | In Chapter 6, we will learn more about linear regression... | Tomás Capretto |
| 191 | The utility of plot_cap ... | The utility of plot_predictions... | Tomás Capretto |
| 194 | model_poly4 = bmb.Model("rented ∼ poly(temperature, degree=4)", bikes, | model_poly4 = bmb.Model("rented ∼ poly(hour, degree=4)", bikes, | Jacob Warren |
| 195 | Figure 6.5: Posterior mean and posterior predictive distribution for the bikes model with temperature and humidity | Figure 6.5: Posterior mean and posterior predictive distribution for the polynomial bikes models with hour. | Jacob Warren |
| 207 | We have been using bmb.interpret_plot_predictions ... One of them is bmb.interpret_plot_comparisons. | We have been using bmb.interpret.plot_predictions ... One of them is bmb.interpret.plot_comparisons. | Tomás Capretto |
| 208 | Another useful function is bmb.interpret_plot_slopes | Another useful function is bmb.interpret.plot_slopes | Tomás Capretto |
| 254 | We call 𝜙 the inverse link function and 𝜙 is... | We call 𝜓 the inverse link function and 𝜙 is... | Jacob Warren |
Note: on page 23, Figure 1.9 shows the kurtosis. What PreliZ actually computes is the "excess kurtosis", i.e the kurtosis -3. Thanks to Narinder Singh for pointing this out.