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1

In versions 11.+, we can use System`DateListPlotDump`DateTicks to get date ticks: SeedRandom[2021]; data = Transpose[{RandomReal[{3250368000, 3250454400}, 10000], RandomReal[{0, 100}, 10000]}]; SmoothHistogram3D[data, ImageSize -> Large, Ticks -> {System`DateListPlotDump`DateTicks[{##}, 7, {"MonthNameShort", " ", &...


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You can use SmoothKernelDistribution with pts and extract the properties "InterpolationPoints" and "PDFValues" and combine them: skd = SmoothKernelDistribution[pts] xyz = Join[Tuples[skd["InterpolationPoints"]], List /@ skd["PDFValues"], 2]; Row[{Labeled[SmoothDensityHistogram[pts, PlotRange -> All, ImageSize ->...


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I believe the proposition that entropy cannot be negative applies only to categorical distributions, which are discrete distributions for which the categories have no intrinsic numerical values. For example, the outcome of a (stylized) coin flip can be characterized by two categories: Heads and Tails. One can associate the numbers 1 and 0 with those ...


3

If you don't mind having histograms on left and bottom frames you can use DensityHistogram with the Method suboption "DistributionAxes". With this approach, in addition to histograms, you can have box-whisker chart, smooth histogram or data rug to represent the marginal distributions of input data: SeedRandom[1] data = RandomReal[...


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The form ImageSize -> a -> b (still undocumented) makes a user units correspond to b printer's points. (See also: this q/a) Play with different values of b to have plots fit into the containing object without being clipped. sizeinprinterpoints = 165; plots = Table[Histogram[driftMatrix[i][[j]], binBoundaries, "PDF", PlotRange -> {{0, ...


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It seems that most of the probability mass of $p$ is between $15$ and $40$. Try: NIntegrate[-(p) Log[p], {x, 15, 40}] (*ans = 2.95531*) Your getting errors because Mathematica is blindly trying to numberically integrate extremely small real numbers outside this region. Check out the piecewise representation of $p$ You can see that the PDF is basically ...


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