{ "cells": [ { "cell_type": "markdown", "id": "d30aa999", "metadata": {}, "source": [ "# Sunspots: the 20th-century \"Grand Maximum\"\n", "\n", "This example applies *bayesloop* to an open question in solar physics. The Sun's\n", "activity rises and falls in a ~11-year cycle, but the strength of successive cycles\n", "also wanders. The mid-20th-century cycles (18–22, ≈1947–2000) were unusually strong,\n", "forming the \"Modern Grand Maximum\" that Solanki et al. (*Nature*, 2004) argued was\n", "the most active the Sun had been in 8,000 years. When SILSO recalibrated the entire\n", "sunspot-number series in 2015 (Clette et al.), however, the apparent uniqueness of\n", "that maximum was sharply reduced. The most recent cycles, 24–25, are the weakest in a\n", "century.\n", "\n", "The question is whether the sequence of cycle amplitudes really contains a secular\n", "*envelope* (a genuine rise-and-fall in cycle strength), or whether it is a constant\n", "average overlaid with large random cycle-to-cycle scatter. This is a problem of model\n", "selection, for which *bayesloop* is well suited. We reduce the oscillating record to\n", "one number per cycle, the peak amplitude, and then let *bayesloop* score three\n", "hypotheses by their marginal likelihood. In the course of the analysis we use the\n", "`Gaussian` observation model, a `HyperStudy` for the time-varying envelope, a\n", "`ChangepointStudy`, the `Static`, `GaussianRandomWalk` and `ChangePoint` transition\n", "models, and a custom flat-prior observation model for a prior-sensitivity check." ] }, { "cell_type": "code", "execution_count": 1, "id": "23a7d447", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:25.178144Z", "iopub.status.busy": "2026-06-12T09:53:25.177879Z", "iopub.status.idle": "2026-06-12T09:53:26.512427Z", "shell.execute_reply": "2026-06-12T09:53:26.512015Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import io\n", "import urllib.request\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "import bayesloop as bl\n", "\n", "try:\n", " import seaborn as sns\n", " sns.set_style(\"whitegrid\")\n", "except ImportError:\n", " plt.style.use(\"default\")\n", "mpl.rcParams.update({\n", " \"figure.dpi\": 110, \"font.size\": 10.5, \"axes.titlesize\": 12,\n", " \"axes.titleweight\": \"bold\", \"axes.edgecolor\": \"#444444\", \"grid.color\": \"#d9d9d9\",\n", "})\n", "C = {\"data\": \"#8a8a8a\", \"accent\": \"#c1272d\", \"accent2\": \"#0b6e99\",\n", " \"green\": \"#2e7d32\", \"amber\": \"#e8a33d\", \"muted\": \"#8a8a8a\",\n", " \"band\": \"#c1272d\", \"band2\": \"#0b6e99\"}\n", "\n", "\n", "def posterior_band(S, name, lo=0.05, hi=0.95):\n", " \"\"\"Posterior mean and an equal-tailed credible band for a time-varying parameter.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mean = (p * x[None, :]).sum(axis=1)\n", " cdf = np.cumsum(p, axis=1)\n", " low = np.array([np.interp(lo, cdf[t], x) for t in range(p.shape[0])])\n", " high = np.array([np.interp(hi, cdf[t], x) for t in range(p.shape[0])])\n", " return mean, low, high\n", "\n", "\n", "def prob_in(S, name, lo=None, hi=None):\n", " \"\"\"Posterior probability the parameter lies in (lo, hi] at each time step.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mask = np.ones_like(x, dtype=bool)\n", " if lo is not None:\n", " mask &= x > lo\n", " if hi is not None:\n", " mask &= x <= hi\n", " return p[:, mask].sum(axis=1)" ] }, { "cell_type": "markdown", "id": "f47c9b50", "metadata": {}, "source": [ "## The data\n", "\n", "We use the yearly mean total sunspot number from\n", "[SILSO](https://www.sidc.be/SILSO/datafiles) (the World Data Center at the Royal\n", "Observatory of Belgium), the canonical 1700–present record. It is a semicolon-separated\n", "CSV with no header; the first two columns are the fractional year and the yearly mean\n", "sunspot number.\n", "\n", "From this series we extract the peak amplitude of each solar cycle since Cycle 1\n", "(≈1750) by locating the cycle maxima with `scipy.signal.find_peaks` (minimum spacing\n", "of 7 years, prominence 20). Working with cycle amplitudes, rather than the raw\n", "oscillating series, isolates exactly the quantity the scientific debate is about." ] }, { "cell_type": "code", "execution_count": 2, "id": "3be9c94b", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:26.514012Z", "iopub.status.busy": "2026-06-12T09:53:26.513905Z", "iopub.status.idle": "2026-06-12T09:53:26.939362Z", "shell.execute_reply": "2026-06-12T09:53:26.938344Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26 solar cycles (1750-): mean amplitude 170, range 76-269\n" ] } ], "source": [ "from scipy.signal import find_peaks\n", "\n", "urls = [\"https://www.sidc.be/SILSO/INFO/snytotcsv.php\",\n", " \"https://www.sidc.be/silso/INFO/snytotcsv.php\"]\n", "raw = None\n", "for u in urls:\n", " try:\n", " req = urllib.request.Request(u, headers={\"User-Agent\": \"Mozilla/5.0 (bayesloop docs)\"})\n", " raw = urllib.request.urlopen(req, timeout=90).read()\n", " break\n", " except Exception: # noqa: BLE001\n", " continue\n", "\n", "df = pd.read_csv(io.BytesIO(raw), sep=\";\", header=None,\n", " names=[\"frac_year\", \"ssn\", \"std\", \"nobs\", \"flag\"])\n", "df[\"year\"] = df[\"frac_year\"].astype(int)\n", "df = df[(df[\"year\"] >= 1700) & (df[\"year\"] <= 2025)].reset_index(drop=True)\n", "yr = df[\"year\"].values\n", "ssn = df[\"ssn\"].values\n", "\n", "# Solar-cycle peak amplitudes (reliable telescopic era, Cycle 1 onward ~1750).\n", "pk, _ = find_peaks(ssn, distance=7, prominence=20)\n", "pk = pk[yr[pk] >= 1750]\n", "peak_years = yr[pk].astype(float)\n", "amp = ssn[pk].astype(float)\n", "print(f\"{len(amp)} solar cycles (1750-): mean amplitude {amp.mean():.0f}, \"\n", " f\"range {amp.min():.0f}-{amp.max():.0f}\")" ] }, { "cell_type": "markdown", "id": "78c96319", "metadata": {}, "source": [ "## A Gaussian model with a time-varying envelope\n", "\n", "We model the amplitude sequence with a `Gaussian` observation model: each cycle's\n", "peak amplitude is Gaussian about a slowly varying expected amplitude (`amp`) with an\n", "inferred cycle-to-cycle scatter (`scatter`). The scatter parameter makes the large\n", "intrinsic variability of solar cycles explicit and lets the model decide how much of\n", "the wandering is signal and how much is noise.\n", "\n", "```python\n", "bl.om.Gaussian(\"amp\", ..., \"scatter\", ...)\n", "```\n", "\n", "For the dynamics of the mean amplitude we first allow a secular envelope: a\n", "`GaussianRandomWalk` on `amp`, wrapped in a `HyperStudy` that marginalises over the\n", "unknown step size (`sigma`) of that drift." ] }, { "cell_type": "code", "execution_count": 3, "id": "46ebc502", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:26.941283Z", "iopub.status.busy": "2026-06-12T09:53:26.941115Z", "iopub.status.idle": "2026-06-12T09:53:27.972535Z", "shell.execute_reply": "2026-06-12T09:53:27.972161Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Gaussian observations. Parameter(s): ['amp', 'scatter']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "! WARNING: Time stamps are not equally spaced. This may result in false plotting of parameter distributions.\n" ] } ], "source": [ "def gauss():\n", " return bl.om.Gaussian(\"amp\", bl.cint(40, 300, 220), \"scatter\", bl.oint(5, 110, 60))\n", "\n", "\n", "# ---- Time-varying amplitude envelope --------------------------------------\n", "Se = bl.HyperStudy()\n", "Se.load_data(amp, timestamps=peak_years)\n", "Se.set(gauss(), bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 45, 30), target=\"amp\"))\n", "Se.fit(silent=True)\n", "env_m, env_lo, env_hi = posterior_band(Se, \"amp\")" ] }, { "cell_type": "markdown", "id": "56883ed8", "metadata": {}, "source": [ "## Comparing an envelope with a constant mean\n", "\n", "The envelope above assumes that the cycle strength drifts, and it is not yet clear\n", "whether the data support that assumption. Because every model here shares the\n", "identical observation model and data, their log₁₀ marginal likelihoods\n", "(`S.log10_evidence`) are directly comparable. We compare three hypotheses:\n", "\n", "| Hypothesis | Transition model | Question |\n", "|---|---|---|\n", "| Constant mean (null) | `tm.Static()` | one average amplitude + random scatter |\n", "| Time-varying envelope | `tm.GaussianRandomWalk` (HyperStudy) | a genuine secular rise-and-fall |\n", "| Single change-point | `tm.ChangePoint` (ChangepointStudy) | one regime shift in cycle strength |" ] }, { "cell_type": "code", "execution_count": 4, "id": "f07e974c", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:27.973805Z", "iopub.status.busy": "2026-06-12T09:53:27.973723Z", "iopub.status.idle": "2026-06-12T09:53:28.128991Z", "shell.execute_reply": "2026-06-12T09:53:28.128471Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Gaussian observations. Parameter(s): ['amp', 'scatter']\n", "+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n", "+ Created new study.\n", " --> Hyper-study\n", " --> Change-point analysis\n", "+ Successfully imported array.\n", "+ Observation model: Gaussian observations. Parameter(s): ['amp', 'scatter']\n", "+ Transition model: Change-point. Hyper-Parameter(s): ['t_change']\n", "+ Detected 1 change-point(s) in transition model: ['t_change']\n", "+ Set hyper-prior(s): ['uniform']\n" ] } ], "source": [ "# ---- Model selection: secular envelope vs constant mean -------------------\n", "S0 = bl.Study(); S0.load_data(amp, timestamps=peak_years); S0.set(gauss(), bl.tm.Static()); S0.fit(silent=True)\n", "Scp = bl.ChangepointStudy(); Scp.load_data(amp, timestamps=peak_years)\n", "Scp.set(gauss(), bl.tm.ChangePoint(\"t_change\", \"all\")); Scp.fit(silent=True)\n", "cp_x, cp_p = Scp.get_hyper_parameter_distribution(\"t_change\")\n", "\n", "evid = {\n", " \"Constant mean\": float(S0.log10_evidence),\n", " \"Time-varying envelope\": float(Se.log10_evidence),\n", " \"Single change-point\": float(Scp.log10_evidence),\n", "}\n", "best = max(evid, key=evid.get)" ] }, { "cell_type": "markdown", "id": "16129710", "metadata": {}, "source": [ "### Robustness of the verdict to the prior\n", "\n", "When models are this close, the conclusion could in principle depend on the prior. We\n", "repeat the constant-versus-envelope comparison with a deliberately flat prior on the\n", "Gaussian parameters (supplied as a one-line `prior=` lambda on the observation model)\n", "and check that the sign of the evidence difference does not flip." ] }, { "cell_type": "code", "execution_count": 5, "id": "06b52e83", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:28.130098Z", "iopub.status.busy": "2026-06-12T09:53:28.130019Z", "iopub.status.idle": "2026-06-12T09:53:29.175103Z", "shell.execute_reply": "2026-06-12T09:53:29.174603Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Gaussian observations. Parameter(s): ['amp', 'scatter']\n", "+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n", "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Gaussian observations. Parameter(s): ['amp', 'scatter']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] } ], "source": [ "def gauss_flat():\n", " return bl.om.Gaussian(\"amp\", bl.cint(40, 300, 220), \"scatter\", bl.oint(5, 110, 60),\n", " prior=lambda amp, scatter: np.ones_like(amp))\n", "\n", "\n", "S0f = bl.Study(); S0f.load_data(amp, timestamps=peak_years); S0f.set(gauss_flat(), bl.tm.Static()); S0f.fit(silent=True)\n", "Sef = bl.HyperStudy(); Sef.load_data(amp, timestamps=peak_years)\n", "Sef.set(gauss_flat(), bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 45, 30), target=\"amp\")); Sef.fit(silent=True)\n", "prior_sensitivity = {\n", " \"jeffreys\": float(S0.log10_evidence - Se.log10_evidence),\n", " \"flat\": float(S0f.log10_evidence - Sef.log10_evidence),\n", "}" ] }, { "cell_type": "markdown", "id": "7801eedd", "metadata": {}, "source": [ "## Reading off the eras\n", "\n", "Finally we quote some model-free magnitudes directly from the amplitude series, namely\n", "the Modern Maximum, the Dalton Minimum and the recent decline, together with the\n", "inferred envelope level at two reference cycles (≈1905 and ≈1957), so we can see how\n", "high the secular envelope climbs through the 20th century." ] }, { "cell_type": "code", "execution_count": 6, "id": "be63a889", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:29.176407Z", "iopub.status.busy": "2026-06-12T09:53:29.176322Z", "iopub.status.idle": "2026-06-12T09:53:29.179425Z", "shell.execute_reply": "2026-06-12T09:53:29.178995Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MODEL EVIDENCE (log10):\n", " Constant mean -63.11\n", " Time-varying envelope -63.38\n", " Single change-point -63.49\n", " best: Constant mean\n", "Long-term mean amplitude 170; Modern Max (1947-2000) 207; Dalton (1804-16) 78; recent (>=2008) 134\n", "Modern-Max excess over long-term mean: +37\n", "Inferred envelope: 1905 ~ 153 -> 1957 ~ 197\n", "Prior sensitivity (constant - envelope, log10): Jeffreys +0.27, flat +0.38 (both narrowly favour constant)\n" ] } ], "source": [ "# Modern Maximum (cycles peaking 1947-2000) vs long-term; Dalton Minimum; recent.\n", "mm = amp[(peak_years >= 1945) & (peak_years <= 2002)]\n", "dalton = amp[(peak_years >= 1798) & (peak_years <= 1825)]\n", "recent = amp[peak_years >= 2008]\n", "\n", "# Inferred envelope level at two reference cycles.\n", "i1900 = np.argmin(np.abs(peak_years - 1905))\n", "i1960 = np.argmin(np.abs(peak_years - 1957))\n", "\n", "print(\"\\nMODEL EVIDENCE (log10):\")\n", "for k, v in evid.items():\n", " print(f\" {k:24s} {v:8.2f}\")\n", "print(f\" best: {best}\")\n", "print(f\"Long-term mean amplitude {amp.mean():.0f}; Modern Max (1947-2000) {mm.mean():.0f}; \"\n", " f\"Dalton (1804-16) {dalton.mean():.0f}; recent (>=2008) {recent.mean():.0f}\")\n", "print(f\"Modern-Max excess over long-term mean: {mm.mean() - amp.mean():+.0f}\")\n", "print(f\"Inferred envelope: 1905 ~ {env_m[i1900]:.0f} -> 1957 ~ {env_m[i1960]:.0f}\")\n", "print(f\"Prior sensitivity (constant - envelope, log10): \"\n", " f\"Jeffreys {prior_sensitivity['jeffreys']:+.2f}, \"\n", " f\"flat {prior_sensitivity['flat']:+.2f} (both narrowly favour constant)\")" ] }, { "cell_type": "markdown", "id": "d2a84c26", "metadata": {}, "source": [ "The model evidence settles the question. The three models land within ≲ 0.4 log₁₀ of\n", "one another, and the constant-mean model narrowly wins. The conclusion is therefore\n", "that there is no compelling statistical evidence that the cycle-amplitude envelope\n", "varies: the entire 26-cycle sequence, Dalton Minimum and Modern Maximum included, is\n", "barely distinguishable from a constant mean of ~170 with large (~50) random\n", "cycle-to-cycle scatter. The verdict is robust to the prior (the constant model leads\n", "by roughly +0.4 log₁₀ under a flat prior versus +0.3 under the default Jeffreys\n", "prior), so it does not depend on that choice.\n", "\n", "Quantitatively, the Modern-Maximum cycles (1947–2000) averaged ~207 versus the\n", "long-term ~170, an excess of only ~+37, which is less than one cycle-to-cycle\n", "standard deviation. The Dalton Minimum (~78) is the single most pronounced excursion,\n", "and it is the only feature the envelope resolves with much confidence." ] }, { "cell_type": "markdown", "id": "bec216ae", "metadata": {}, "source": [ "## Visualising the record and the envelope\n", "\n", "The top panel shows the familiar three-century sunspot record with the per-cycle peak\n", "amplitudes marked; the bottom panel shows those amplitudes with the inferred envelope\n", "and its 90% credible band, against the long-term mean. The shaded spans mark the\n", "Dalton Minimum, the Modern Grand Maximum, and the recent decline." ] }, { "cell_type": "code", "execution_count": 7, "id": "4df1162c", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:29.180589Z", "iopub.status.busy": "2026-06-12T09:53:29.180513Z", "iopub.status.idle": "2026-06-12T09:53:29.279190Z", "shell.execute_reply": "2026-06-12T09:53:29.278695Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9.2, 5.6), sharex=True)\n", "ax1.fill_between(yr, 0, ssn, color=C[\"amber\"], alpha=0.55, lw=0, label=\"yearly sunspot number\")\n", "ax1.plot(peak_years, amp, \"o\", ms=4, color=C[\"accent\"], label=\"cycle peak amplitude\")\n", "ax1.set_ylabel(\"sunspot number\")\n", "ax1.set_title(\"Three centuries of solar activity: is the mid-20th-century maximum exceptional?\")\n", "ax1.legend(loc=\"upper left\", fontsize=8)\n", "ax1.set_ylim(0, 300)\n", "\n", "ax2.plot(peak_years, amp, \"o-\", ms=4, color=C[\"muted\"], lw=0.8, label=\"cycle peak amplitude\")\n", "ax2.plot(peak_years, env_m, color=C[\"accent\"], lw=2.4, label=\"inferred envelope (posterior mean)\")\n", "ax2.fill_between(peak_years, env_lo, env_hi, color=C[\"band\"], alpha=0.2, label=\"90% credible band\")\n", "ax2.axhline(amp.mean(), color=\"k\", ls=\"--\", lw=1, label=f\"long-term mean {amp.mean():.0f}\")\n", "for x0, x1, txt, yy in [(1798, 1825, \"Dalton\\nMinimum\", 60),\n", " (1945, 2002, \"Modern Grand\\nMaximum\", 250),\n", " (2008, 2025, \"recent\\ndecline\", 90)]:\n", " ax2.axvspan(x0, x1, color=C[\"accent2\"], alpha=0.07)\n", " ax2.text((x0 + x1) / 2, yy, txt, ha=\"center\", fontsize=7.5, color=C[\"accent2\"])\n", "ax2.set_ylabel(\"cycle amplitude\")\n", "ax2.set_xlabel(\"year\")\n", "ax2.set_xlim(1748, 2028)\n", "ax2.legend(loc=\"lower left\", ncol=2, fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "092d47dd", "metadata": {}, "source": [ "The envelope dips during the Dalton Minimum (~1800–1820, down to ~80), rises through\n", "the Modern Grand Maximum (~200 around 1957), and falls again into the recent decline\n", "(cycles 24–25). As the evidence comparison below makes explicit, however, that\n", "visually salient rise-and-fall is not strong enough to beat a flat line once the\n", "Occam penalty for the extra flexibility is paid." ] }, { "cell_type": "code", "execution_count": 8, "id": "d50609a7", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:53:29.280351Z", "iopub.status.busy": "2026-06-12T09:53:29.280259Z", "iopub.status.idle": "2026-06-12T09:53:29.367740Z", "shell.execute_reply": "2026-06-12T09:53:29.367252Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(7.4, 3.4))\n", "labels = list(evid.keys())\n", "vals = np.array([evid[k] for k in labels]) - min(evid.values())\n", "ax.barh(labels, vals, color=[C[\"muted\"], C[\"accent\"], C[\"green\"]])\n", "ax.set_xlabel(r\"log$_{10}$ evidence vs worst\")\n", "ax.set_title(\"Does the cycle-amplitude envelope really vary?\")\n", "for i, v in enumerate(vals):\n", " ax.text(v, i, f\" {v:.2f}\", va=\"center\", fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e9832163", "metadata": {}, "source": [ "## The scientific conclusion\n", "\n", "*bayesloop* sides with the post-recalibration view (Clette et al., 2014; Usoskin et\n", "al.): once the large intrinsic variability of solar cycles is accounted for, the\n", "Modern Grand Maximum is not statistically exceptional. It is a run of strong cycles\n", "within the normal range of solar variability, not evidence of a fundamentally\n", "different solar state. The Dalton Minimum stands out more clearly than the Modern\n", "Maximum. This is a textbook example of a striking-looking feature failing a rigorous\n", "significance test.\n", "\n", "The amplitudes are taken from the yearly SILSO v2.0 series, which slightly understates\n", "true cycle maxima (smoothed monthly values are the convention) but does so\n", "consistently across cycles; pre-1749 cycles are excluded as less reliable.\n", "\n", "## What this example showed\n", "\n", "On a single dataset we used *bayesloop* to:\n", "\n", "- weigh an open and recently reopened question (Solanki 2004 versus the 2015\n", " recalibration) with a transparent model-evidence calculation rather than by\n", " eyeballing a curve;\n", "- obtain a result in which the simpler model wins, with the constant-mean model\n", " narrowly beating both a time-varying envelope (`HyperStudy` + `GaussianRandomWalk`)\n", " and a single `ChangePoint`, showing that *bayesloop*'s model selection resists\n", " over-interpreting visually salient features;\n", "- model the right quantity, reducing the oscillating record to per-cycle amplitudes\n", " so that the analysis targets exactly the debated quantity, with the inferred\n", " `scatter` parameter making the large intrinsic variability explicit;\n", "- test the verdict against the prior with a one-line custom flat-prior `Gaussian`\n", " observation model, confirming that the narrow result does not depend on the prior\n", " choice." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "hydrogen" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }