Re: Unable to generate citations in markdown_strict

[kiran kumar <krankumar-Re5JQEeQqe8AvxtiuMwx3w@public.gmane.org>]

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I am also trying to add math in this format within the markdown
$$ 
Experimentalquestion:
\underbrace{
    \begin{cases} H: statistic_{misrepresentation} \neq 
statistic_{intentional}
     H0: statistic_{misrepresentation} = statistic_{intentional} 
end{cases} 
\text{verify truth of both statements}} 
{\text{equality/inequality with an acceptable margin of statistical error}} 
$$

This is not rendered as expected. Can you guide me on getting this format 
to work? 
On Wednesday, 16 June 2021 at 12:34:43 UTC-7 John MacFarlane wrote:

>
> markdown_strict doesn't support citations (that's an extension).
> Try markdown.
>
> kiran kumar <kran...-Re5JQEeQqe8AvxtiuMwx3w@public.gmane.org> writes:
>
> > 
> >
> > Using the following command to generate citations
> >
> > pandoc test.md -citeproc -f markdown_strict+yaml_metadata_block -t 
> > markdown_strict+citations+smart+yaml_metadata_block -s --bibliography 
> > blog.bib --csl acm.csl -o check.md
> >
> > The test.md has a few citations but it is not rendered as references in 
> the 
> > check.md
> >
> > Is there something I am missing?
> >
> > -- 
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> https://groups.google.com/d/msgid/pandoc-discuss/f0b66f7d-b530-4c2e-9979-2fd40ff51dd4n%40googlegroups.com
> .
> > ---
> > bibliography: blog.bib
> > csl: acm.csl
> > date: "2020-10-128T20:20:00Z"
> > draft: true
> > title: Process of Data science - Measurement
> > ---
> >
> > # Measurement variables
> >
> > In a previous post, the process of data science and forming an
> > hypothesis is discussed. A hypothesis is the relevant to align a
> > business objective to a data science problem. The hypothesis provides a
> > "big-picture" view of the issues which need to considered in further
> > steps of addressing a data science problem.
> >
> > The problem being considered is insurance fraud, and a good hypothesis
> > for success could be “misrepresentation is different from intentional
> > damage”. This hypothesis attempts to differentiate between
> > misrepresentation and intentional damage.
> >
> >> Misrepresentiation is said to occur when a claim is made on
> >> nonexistent assets
> >>
> >> Intentional damage is said to occur when an insured asset is
> >> intentionally damaged
> >
> > The next step after an hypothesis is established is to consider
> > variables or factors affecting the hypothesis.
> >
> > 1. [Hypothesis](http://knkumar.com/blog/posts/data_science_process/)
> > 2. Measurement variables (discussed here)
> > 3. Latent or unobservable factors
> > 4. Experimental design (0 to 1)
> > 1. Controlling other factors to observe primary effect.
> > 5. Collection and analysis of data for pattern discovery
> > 1. Hypothesis driven Exploration
> > 6. Modeling of patterns for prediction
> > 1. Numerical Analysis for error reduction
> > 2. Qualitative modeling
> > 7. Generalizing or scaling the experiment (1 to n)
> > 8. Establishing a baseline
> > 9. Monitoring through controls and baselines
> > 10. Ethics and governance
> >
> > ## The null Hypothesis
> >
> > Let us call our hypothesis “misrepresentation is different from
> > intentional damage” - $H$ for mathematical convenience. This can be a
> > hard thing to determine and we can use ideas from *statistical testing*
> > to develop a solution. A statistical testing process works by
> > determining an antithesis often called the null hypothesis, i.e., if the
> > antithesis were true the hypothesis under consideration would not be
> > true. An antithesis could be "misrepresentation is indifferentiable from
> > intentional damage", call this $H\_0$.
> >
> > In a traditional scientific experiment, a statistical experiment would
> > be possible by random assignment to conditions under test. In this
> > scenario, one group of insured would generate misrepresentation whereas
> > another group would generate intentional damage claims. Traditional
> > hypothesis testing would calculate a statistic, say a mean, for data
> > generated from two groups and observe if statistic is significantly
> > different from each other. $$ Experimental\\ question:
> > \\underbrace{\\begin{cases} H: statistic*{misrepresentation}\\neq
> > statistic*{intentional}\\ H*0: statistic*{misrepresentation} =
> > statistic*{intentional} \\end{cases} \\text{verify truth of both
> > statements}}* {\\text{equality/inequality with an acceptable margin of
> > statistical error}} $$ In this scenario, misrepresentation and
> > intentional damange are not randomly assigned or generated from insured
> > parties. In fact, it would be facetious to conduct an experiment to
> > study the problem at hand. Such a problem falls under the umbrella of a
> > natural experiment or observational study depending on the circles you
> > are in.
> >
> > In an observational study the assignment of population to groups or
> > conditions of the experiment are outside the investigator's purview. A
> > hypothesis such as "smoking causes cancer" or "video games cause
> > violence" [@engelhardt2011your] is harder to perform in a pure
> > scientific manner. In fact, the earlier position on video games by
> > [@engelhardt2011your] has been attributed to priming by
> > [@kuhn2019does] and the jury could still be out on this since we
> > cannot guarantee homogenity of the sample in testing for observed
> > effects. In such scenarios the best we can do are observational studies
> > to gain more information about our hypothesis.
> >
> > ## What are Measurement Variables (aka Direct Factors)?
> >
> > In order to perform a *scientific study*, a data scientist should start
> > by picking up on *signals* of misrepresentation and intentional damage.
> > These signals are often referred to as measurement variables for
> > modeling. The model of choice for such a problem is a discriminative
> > model, i.e., a model discriminating fraud of misrepresentation and
> > intentional damage. In the old but popular example of discriminating the
> > iris species [@fisher1936use], the petal length/width and sepal
> > length/width provided sufficient measurement variables for
> > discrimination of the species using linear functions. In this iris
> > analysis, the experiment was natural, i.e., not in the control of an
> > experimenter.
> >
> > The term ***natural*** means the experimenter did not genetically modify
> > the species to show variations, the variation in the species was
> > naturally selected. On the other hand, in cases such as experiments with
> > [fruit flies](https://bdsc.indiana.edu/about/index.html) (available at
> > Indiana University for research), a scientist would study the species by
> > "knocking out genes" or "inducing variations" creating a *controlled*
> > experiment. The key in either case would be understanding the *factors*
> > or **measurement variables** for the hypothesis under study.
> >
> > A **natural/observational experiment** is a useful alternative when a
> > controlled experiment cannot be undertaken like the insurance example.
> > It is important to note that a natural experiment can also have issues
> > regarding confounding variables and bias which potentially invalidate
> > the experiment.
> >
> > A ***confound*** (or confounding variable) can be defined as a factor
> > which could directly or indirectly affect the response variable when
> > considering a direct measurement. Let's take a concrete example here to
> > understand this concept. Assume a scout is looking for talent in
> > basketball (or a VC firm is scouting for investment, the analogy is
> > similar). The scout assesses the talent using a few metrics such as
> > average points per game, assists for offense and rebounds, block, steals
> > for defense. There are *other aspects* (or confounds) which come into
> > the purview of a scout, such as medical history and
> > stability/improvement of stats because these indicate the progression of
> > a player and future outcomes. In many cases, a *confound* plays a large
> > role. For example, a player with a debilitating shoulder injury could be
> > a red flag since the future outcome could be weaker with a higher
> > probability. The difficulty would be in ascertaining confounds for the
> > hypothesis under study, and requires understanding the true nature of
> > the effect a confound has on the hypothesis. A *targeted interview* with
> > an expert (such as claims investigator for insurance or talent scout for
> > sports) is a valuable tool in a data scientists arsenal to understand
> > the factors and confounds which should be considered as data to be
> > included in a model. An interview provides the intuition or priors in a
> > bayesian context for data gathering and evaluation.
> >
> > A variable or factor discriminating ***misrepresentation*** from
> > ***intentional damage*** could be identified based on multiple
> > perspectives. Personally, I choose the word perspective as a line of
> > attack/strategy to understand the contributing factors from first
> > principles. This is a preferred approach, in my opinion, to throwing the
> > kitchen sink at a dataset.
> >
> > #### Historical variables
> >
> > Historical variables can be obtained from similar category of claims in
> > the past. They are useful in understanding patterns of normal insurance
> > claims and misrepresentation. Cost per type of damage could be a general
> > factor to monitor, which needs categorizing types of damage available in
> > historical data. In many cases, the insurance system would place
> > restrictions on type of damages covered and bundle similar damages under
> > a large umbrella (because its easier to deal with one type and have a
> > single process). For example flooding could be due to natural events
> > like weather (rain, storm, waves, etc) or a pipe breaking due to stress
> > or damage. Classifying the category at the right level is important in
> > order to provide models the right level of information, not focusing on
> > data driven approaches when collecting data can *misclassify* labels by
> > not having appropriate levels for a category losing a lot of context.
> >
> > #### Textual variables
> >
> > Textual variables can be obtained from an insurance claim which asks
> > pointed questions to a claimant. Many of the responses to the questions
> > can be free form text or speech which allow representation of the
> > situation in the claim. A misrepresented claim can potentially have
> > signals in the text to describe the situation. Simple constructs would
> > be overuse of certain elements to provide validity to the claim. A
> > speech pattern can have inflection when misrepresenting facts which can
> > be captured by a model.
> >
> > Another common pattern to obtain signals be asking the same question
> > with a different phrase. Text or speech patterns for both questions
> > should ideally be the similar and a measure of dissimilarity can be used
> > by a model to discriminate between misrepresentation and intentional
> > damage. The details of spacing between the questions and phrasing are
> > experimental variables at the hands of the data scientist to gather
> > useful signals.
> >
> > #### Social variables
> >
> > Social variables can be obtained from aspects of social interaction such
> > as association to similar groups, participation in similar events or
> > mining social media sites such as Facebook, Twitter, Snapchat etc. The
> > usage of social variables stem from the phrase - "neurons that are fire
> > together wire together" implying that if there is a person who filed a
> > claim with misrepresentation or intentional damage another person could
> > be correlated to do so through social bonds.
> >
> > Personally, I am not a proponent of using social variables but in some
> > cases they can provide useful information akin to a prior for the model.
> > A data scientist needs to be careful in ensuring the prior or social
> > variables can be overcome by evidence in either direction.
> >
> > #### Economic variables
> >
> > ## Identifying measurement variables
> >
> > ### Correlation
> >
> > ### Separation of classes
> > # articles for reinforcement learning
> > @article{vinyals2017starcraft,
> > title={Starcraft ii: A new challenge for reinforcement learning},
> > author={Vinyals, Oriol and Ewalds, Timo and Bartunov, Sergey and 
> Georgiev, Petko and Vezhnevets, Alexander Sasha and Yeo, Michelle and 
> Makhzani, Alireza and K{\"u}ttler, Heinrich and Agapiou, John and 
> Schrittwieser, Julian and others},
> > journal={arXiv preprint arXiv:1708.04782},
> > url={https://arxiv.org/pdf/1708.04782},
> > year={2017}
> > }
> > @article{dulac2019challenges,
> > title={Challenges of real-world reinforcement learning},
> > author={Dulac-Arnold, Gabriel and Mankowitz, Daniel and Hester, Todd},
> > journal={arXiv preprint arXiv:1904.12901},
> > url={https://arxiv.org/pdf/1904.12901},
> > year={2019}
> > }
> >
> > # articles on data science
> > @article{engelhardt2011your,
> > title={This is your brain on violent video games: Neural desensitization 
> to violence predicts increased aggression following violent video game 
> exposure},
> > author={Engelhardt, Christopher R and Bartholow, Bruce D and Kerr, 
> Geoffrey T and Bushman, Brad J},
> > journal={Journal of Experimental Social Psychology},
> > volume={47},
> > number={5},
> > pages={1033--1036},
> > year={2011},
> > url={https://hal.archives-ouvertes.fr/peer-00995254/document},
> > publisher={Elsevier}
> > }
> > @article{kuhn2019does,
> > title={Does playing violent video games cause aggression? A longitudinal 
> intervention study},
> > author={K{\"u}hn, Simone and Kugler, Dimitrij Tycho and Schmalen, 
> Katharina and Weichenberger, Markus and Witt, Charlotte and Gallinat, 
> J{\"u}rgen},
> > journal={Molecular psychiatry},
> > volume={24},
> > number={8},
> > pages={1220--1234},
> > year={2019},
> > url={https://www.nature.com/articles/s41380-018-0031-7},
> > publisher={Nature Publishing Group}
> > }
> > @article{fisher1936use,
> > title={The use of multiple measurements in taxonomic problems},
> > author={Fisher, Ronald A},
> > journal={Annals of eugenics},
> > volume={7},
> > number={2},
> > pages={179--188},
> > year={1936},
> > url={
> https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-1809.1936.tb02137.x
> },
> > publisher={Wiley Online Library}
> > }
>

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