Parameter estimation exercises II
About the course
What are we doing?
Basic Python programming review
1
Configuring your computer to use Python for scientific computing
2
Variables, operators, and types
3
Conditionals
4
Lists and tuples
5
Iteration
6
Introduction to functions
7
Methods
8
Dictionaries
9
Packages and modules
10
Errors and Exception handling
11
File I/O
12
Introduction to Numpy and Scipy
Polars and split-apply-combine
13
Introduction to data frames and Polars
14
Tidy data and split-apply-combine
15
Polars for Pandas users
Data display
16
Making plots with Bokeh
17
High level plotting with iqplot
18
Styling Bokeh plots
19
Dealing with overplotting
Probability: The foundation for generative modeling
20
Probability: definitions and interpretations
21
Probability distributions
Sampling out of probability distributions
22
Random number generation
23
Random number generation using Numpy
24
Simulating the Luria-Delbrück distribution
Plug-in estimates and confidence intervals
25
Plug-in estimates
26
Bias
27
Confidence intervals
Nonparametric inference with hacker stats
28
Performing bootstrap calculations
29
Pairs bootstrap and correlation
Null hypothesis significance testing
30
Null hypothesis significance testing
31
Comments and opinions on NHST
32
Hacker’s approach to NHST
Generative modeling
33
Statistical modeling
34
Building a generative model
Parameter estimation
35
Method of moments
36
Maximum likelihood estimation
37
Numerical maximum likelihood estimation
38
Confidence region and confidence intervals for a MLE
39
Parallel bootstrap calculations
40
Mixture models
Variate-covariate models
41
Model building
42
Maximum likelihood estimation for variate-covariate models
43
Implementation of MLE for variate-covariate models
Model assessment
44
Graphical model assessment
45
Model comparison with the Akaike information criterion
46
Graphical model assessment for univariate models
47
Graphical model assessment: Predictive regression
48
Model comparison with the AIC
Statistical watchouts
49
The Olkin-Petkau-Zidek example of MLE fragility
50
Nonidentifiable models
51
Dancing statistics
Basic Python exercises
52
Using string methods{#exr-string-methods}
53
Restriction enzyme cut sites
54
Longest common substring
55
Pathogenicity islands
56
RNA secondary structure validator
57
Computing things!
58
Working with two-dimensional arrays
Polars and split-apply-combine exercises
59
Mastering selection and filtering of data frames
60
Split-Apply-Combine of the frog data set
61
Adding data to a data frame
62
Palmer penguins and split-apply-combine
Data visualization exercises
63
Plotting with Palmer penguins
64
Microtubule catastrophe and ECDFs
65
EDA for a temperature controlled Gal4-UAS system
Probability exercises
66
Distribution stories
67
Exploring tails of distributions
68
Normal approximations
69
Spike timing with a refractory period
70
Simulating microtubule catastrophe times
Nonparametric hacker stats exercises
71
Confidence intervals for microtubule catastrophe
72
Wrangling and hacker stats with Darwin’s finches
NHST exercises
73
NHST with Darwin’s finches
74
Dance of the p-values
Parameter estimation exercises I
75
Modeling and parameter estimation for Boolean data
76
Heavy-tailed distributions and outliers
77
Estimating spiking rate
Parameter estimation exercises II
78
Determining dissociation constants
79
MLE for a ligand-gated ion channel
Model assessment exercises
80
Analysis of microtubule catastrophe
81
Data transformations and parameter estimation
Parameter estimation exercises II
It’s time for more parameter estimation, this time with variate-covariate models!
77
Estimating spiking rate
78
Determining dissociation constants