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Commit 1a2c0f2

Browse files
Correct five minor typos in notebooks (#96)
Edits made using the following bash commands `find . -name "*.ipynb" -exec sed -i '' s/wondereful/wonderful/g {} +` `find . -name "*.ipynb" -exec sed -i '' s/probabilty/probability/g {} +` `find . -name "*.ipynb" -exec sed -i '' s/sensitivy/sensitivity/g {} +` `find . -name "*.ipynb" -exec sed -i "" s/'of of'/'is of'/g {} +` `find . -name "*.ipynb" -exec sed -i "" s/'is know as'/'is known as'/g {} +`
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‎notebooks/01a-instructor-probability-simulation.ipynb‎

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"source": [
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"What type of random phenomena are we talking about here? One example is:\n",
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"\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probabilty of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probability of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- But given the data of how many people click through, how can we calculate the CTR? And how certain can we be of this CTR? Or how likely is a particular CTR?\n",
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"\n",
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"Science mostly asks questions of the second form above & Bayesian thinking provides a wondereful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
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"Science mostly asks questions of the second form above & Bayesian thinking provides a wonderful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
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"\n",
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"We'll first explore questions of the 1st type using simulation: knowing the model, what is the probability of seeing certain data?"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probabilty_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probability_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"cell_type": "markdown",
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"_Discussion point_: This model is know as the bias coin flip. \n",
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"_Discussion point_: This model is known as the bias coin flip. \n",
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"- Can you see why?\n",
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"- Can it be used to model other phenomena?"
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]

‎notebooks/01a-student-probability-simulation.ipynb‎

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"source": [
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"What type of random phenomena are we talking about here? One example is:\n",
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"\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probabilty of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probability of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- But given the data of how many people click through, how can we calculate the CTR? And how certain can we be of this CTR? Or how likely is a particular CTR?\n",
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"\n",
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"Science mostly asks questions of the second form above & Bayesian thinking provides a wondereful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
66+
"Science mostly asks questions of the second form above & Bayesian thinking provides a wonderful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
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"\n",
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"We'll first explore questions of the 1st type using simulation: knowing the model, what is the probability of seeing certain data?"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probabilty_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probability_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"_Discussion point_: This model is know as the bias coin flip. \n",
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"_Discussion point_: This model is known as the bias coin flip. \n",
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"- Can you see why?\n",
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"- Can it be used to model other phenomena?"
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]

‎notebooks/01b-instructor-joint-conditional-probability.ipynb‎

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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch of of species 'fortis'?\""
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch is of species 'fortis'?\""
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‎notebooks/01b-student-joint-conditional-probability.ipynb‎

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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch of of species 'fortis'?\""
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch is of species 'fortis'?\""
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{

‎notebooks/ODSC-East-2020年04月14日/01-Instructor-Probability_a_simulated_introduction.ipynb‎

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"source": [
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"What type of random phenomena are we talking about here? One example is:\n",
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"\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probabilty of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probability of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- But given the data of how many people click through, how can we calculate the CTR? And how certain can we be of this CTR? Or how likely is a particular CTR?\n",
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"\n",
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"Science mostly asks questions of the second form above & Bayesian thinking provides a wondereful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
68+
"Science mostly asks questions of the second form above & Bayesian thinking provides a wonderful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
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"\n",
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"We'll first explore questions of the 1st type using simulation: knowing the model, what is the probability of seeing certain data?"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probabilty_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probability_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"_Discussion point_: This model is know as the bias coin flip. \n",
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"_Discussion point_: This model is known as the bias coin flip. \n",
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"- Can you see why?\n",
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"- Can it be used to model other phenomena?"
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]
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"metadata": {},
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch of of species 'fortis'?\""
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch is of species 'fortis'?\""
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"**Question:** Suppose that a test for using a particular drug has 99% sensitivy (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people are users of the drug. What is the probability that a randomly selected individual with a positive test is a drug user?\n",
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"**Question:** Suppose that a test for using a particular drug has 99% sensitivity (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people are users of the drug. What is the probability that a randomly selected individual with a positive test is a drug user?\n",
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"\n",
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"**If we can answer this, it will be really cool as it shows how we can move from knowing $P(+|user)$ to $P(user|+),ドル a MVP for being able to move from $P(data|model)$ to $P(model|data)$.**"
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]

‎notebooks/ODSC-East-2020年04月14日/01-Student-Probability_a_simulated_introduction.ipynb‎

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"source": [
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"What type of random phenomena are we talking about here? One example is:\n",
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"\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probabilty of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
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"- Knowing that a website has a click-through rate (CTR) of 10%, we can calculate the probability of having 10 people, 9 people, 8 people ... and so on click through, upon drawing 10 people randomly from the population;\n",
6666
"- But given the data of how many people click through, how can we calculate the CTR? And how certain can we be of this CTR? Or how likely is a particular CTR?\n",
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"\n",
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"Science mostly asks questions of the second form above & Bayesian thinking provides a wondereful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
68+
"Science mostly asks questions of the second form above & Bayesian thinking provides a wonderful framework for answering such questions. Essentially Bayes' Theorem gives us a way of moving from the probability of the data given the model (written as $P(data|model)$) to the probability of the model given the data ($P(model|data)$).\n",
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"\n",
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"We'll first explore questions of the 1st type using simulation: knowing the model, what is the probability of seeing certain data?"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probabilty_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"**Note:** Although, in the above, we have described _probability_ in two ways, we have not described it mathematically. We're not going to do so rigorously here, but we will say that _probability_ defines a function from the space of possibilities (in the above, the interval $[0,1]$) that describes how likely it is to get a particular point or region in that space. Mike Betancourt has an elegant [Introduction to Probability Theory (For Scientists and Engineers)](https://betanalpha.github.io/assets/case_studies/probability_theory.html) that I can recommend."
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"_Discussion point_: This model is know as the bias coin flip. \n",
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"_Discussion point_: This model is known as the bias coin flip. \n",
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"- Can you see why?\n",
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"- Can it be used to model other phenomena?"
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch of of species 'fortis'?\""
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"Now that we have a grasp on joint probabilities, lets consider conditional probabilities, that is, the probability of some $A,ドル knowing that some other $B$ is true. We use the notation $P(A|B)$ to denote this. For example, you can ask the question \"What is the probability of a finch beak having depth $<10,ドル knowing that the finch is of species 'fortis'?\""
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"**Question:** Suppose that a test for using a particular drug has 99% sensitivy (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people are users of the drug. What is the probability that a randomly selected individual with a positive test is a drug user?\n",
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"**Question:** Suppose that a test for using a particular drug has 99% sensitivity (true positive rate) and 99% specificity (true negative rate), that is, a 1% false positive rate and 1% false negative rate. Suppose that 0.5% (5 in 1,000) of people are users of the drug. What is the probability that a randomly selected individual with a positive test is a drug user?\n",
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"\n",
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"**If we can answer this, it will be really cool as it shows how we can move from knowing $P(+|user)$ to $P(user|+),ドル a MVP for being able to move from $P(data|model)$ to $P(model|data)$.**"
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]

‎notebooks/ODSC-East-2020年04月14日/02-Instructor-Parameter_estimation_hypothesis_testing.ipynb‎

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"To specify the full probabilty model, you need\n",
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"To specify the full probability model, you need\n",
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"- a likelihood function for the data &\n",
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"- priors for all unknowns.\n",
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‎notebooks/ODSC-East-2020年04月14日/02-Student-Parameter_estimation_hypothesis_testing.ipynb‎

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"To specify the full probabilty model, you need\n",
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"To specify the full probability model, you need\n",
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"- a likelihood function for the data &\n",
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"- priors for all unknowns.\n",
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"\n",

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