markdown_strict doesn't support citations (that's an extension).
Try markdown.
kiran kumar <kran...@gmail.com> 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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> ---
> 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}
> }