{ "cells": [ { "cell_type": "markdown", "id": "5bdd7dbf", "metadata": {}, "source": [ "# The rate of great earthquakes over time\n", "\n", "Between December 2004 and March 2011 the planet produced an unusual run of giant\n", "earthquakes: Sumatra–Andaman (M9.1, 2004), Maule, Chile (M8.8, 2010) and\n", "Tōhoku, Japan (M9.1, 2011), three of the six largest events ever instrumentally\n", "recorded, within seven years. This raised the question of whether the Earth was\n", "entering a more seismically active phase, or whether the cluster was simply a chance\n", "fluctuation. Shearer & Stark (PNAS 2012) and Michael (GRL 2011) argued statistically\n", "for the latter. Here we give an independent answer with *bayesloop*, by letting the\n", "annual rate of great earthquakes vary freely in time and asking whether the data\n", "actually require any such variation.\n", "\n", "This is the mirror image of the measles example. There the evidence clearly\n", "favoured a *changing* parameter; here the answer turns out to be that the\n", "parameter is *constant*, and a useful method must be able to say so. Along the way we\n", "use a representative slice of the toolbox: a `Poisson` observation model under\n", "`Study` (static), `HyperStudy` (marginalising a random-walk step size), `RegimeSwitch`,\n", "and a `ChangepointStudy` scan, all sharing one observation model so that their marginal\n", "evidences are directly comparable and complexity is penalised automatically." ] }, { "cell_type": "code", "execution_count": 1, "id": "3042c2d7", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:26.992577Z", "iopub.status.busy": "2026-06-12T09:52:26.992373Z", "iopub.status.idle": "2026-06-12T09:52:28.342906Z", "shell.execute_reply": "2026-06-12T09:52:28.342461Z" } }, "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": "7223c43b", "metadata": {}, "source": [ "## The data\n", "\n", "We pull the catalogue live from the\n", "[USGS ComCat / FDSN event service](https://earthquake.usgs.gov/fdsnws/event/1/). The\n", "service caps the number of events per request, so we page the query decade by\n", "decade (1900–2025), asking for every M ≥ 7 event and concatenating the CSV chunks.\n", "A short pause between requests keeps the load on the public endpoint light.\n", "\n", "The catalogue is essentially complete for M ≥ 8 (\"great\" earthquakes) back to 1900,\n", "so that is the series we analyse. It is binned into annual counts (0–4 events per\n", "year) and modelled directly with a Poisson observation model.\n", "\n", "> ⚠️ We deliberately analyse M ≥ 8 rather than M ≥ 7. Completeness for M ≥ 7 is\n", "> not uniform over the century: the apparent rise in recent decades is largely a\n", "> detection or catalogue artefact (denser seismometer networks). The M ≥ 8 threshold\n", "> avoids that confound." ] }, { "cell_type": "code", "execution_count": 2, "id": "a632e610", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:28.344399Z", "iopub.status.busy": "2026-06-12T09:52:28.344257Z", "iopub.status.idle": "2026-06-12T09:52:47.771512Z", "shell.execute_reply": "2026-06-12T09:52:47.770596Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " quakes 1900-1909: 148 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1910-1919: 129 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1920-1929: 107 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1930-1939: 115 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1940-1949: 119 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1950-1959: 88 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1960-1969: 126 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1970-1979: 121 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1980-1989: 110 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 1990-1999: 154 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 2000-2009: 143 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 2010-2019: 160 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " quakes 2020-2025: 84 events\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "M>=8.0: 100 events, mean 0.794/yr, dispersion index (var/mean) = 0.97 (Poisson -> 1.0)\n", "M>=8.5 giants: 17 total; 1950-1965 cluster = 7, 2004-2012 cluster = 5\n" ] } ], "source": [ "UA = \"Mozilla/5.0 (bayesloop docs; research; contact chris@karamata-capital.com)\"\n", "\n", "\n", "def fetch_quakes():\n", " \"\"\"Page the USGS FDSN event service by decade and return a concatenated DataFrame.\"\"\"\n", " import time\n", " chunks, header, start = [], None, 1900\n", " while start <= 2025:\n", " end = min(start + 9, 2025)\n", " url = (\n", " \"https://earthquake.usgs.gov/fdsnws/event/1/query?format=csv\"\n", " f\"&starttime={start}-01-01&endtime={end}-12-31\"\n", " \"&minmagnitude=7.0&orderby=time-asc\"\n", " )\n", " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", " txt = (urllib.request.urlopen(req, timeout=120)\n", " .read().decode(\"utf-8\", \"replace\").strip().splitlines())\n", " if txt:\n", " if header is None:\n", " header = txt[0]\n", " chunks.append(header)\n", " chunks.extend(txt[1:])\n", " print(f\" quakes {start}-{end}: {len(txt) - 1} events\")\n", " start = end + 1\n", " time.sleep(1.0)\n", " return pd.read_csv(io.StringIO(\"\\n\".join(chunks)))\n", "\n", "\n", "df = fetch_quakes()\n", "df[\"year\"] = pd.to_datetime(df[\"time\"], format=\"ISO8601\", utc=True).dt.year\n", "df = df[df[\"year\"] <= 2025]\n", "years = np.arange(1900, 2026)\n", "\n", "THR = 8.0\n", "great = df[df[\"mag\"] >= THR]\n", "counts = great.groupby(\"year\").size().reindex(years, fill_value=0).values.astype(float)\n", "mean_rate = counts.mean()\n", "disp = counts.var(ddof=1) / counts.mean() # index of dispersion (==1 for Poisson)\n", "print(f\"\\nM>={THR}: {int(counts.sum())} events, mean {mean_rate:.3f}/yr, \"\n", " f\"dispersion index (var/mean) = {disp:.2f} (Poisson -> 1.0)\")\n", "\n", "# giant (M>=8.5) clusters\n", "giant = df[df[\"mag\"] >= 8.5]\n", "gc = giant.groupby(\"year\").size()\n", "clust_50s = int(gc.loc[(gc.index >= 1950) & (gc.index <= 1965)].sum())\n", "clust_00s = int(gc.loc[(gc.index >= 2004) & (gc.index <= 2012)].sum())\n", "print(f\"M>=8.5 giants: {len(giant)} total; 1950-1965 cluster = {clust_50s}, \"\n", " f\"2004-2012 cluster = {clust_00s}\")" ] }, { "cell_type": "markdown", "id": "622b47d1", "metadata": {}, "source": [ "The annual counts already show a Poisson signature. Their index of\n", "dispersion (variance / mean) is ≈ 1, which is what a homogeneous Poisson process\n", "predicts. By this classical diagnostic alone the counts are indistinguishable\n", "from constant-rate randomness. We note too that the 1950s–60s cluster of M ≥ 8.5\n", "giants is at least as large as the 2004–2012 one, so any account of a recent\n", "increase has to explain away an earlier burst that was just as big (it included the\n", "1960 Chile M9.5, the largest earthquake ever recorded).\n", "\n", "## Model comparison: constant versus time-varying Poisson rate\n", "\n", "The observation model is a single shared `bl.om.Poisson('rate', …)`. We compare three\n", "hypotheses about how the rate behaves over 126 years, and add a\n", "change-point scan afterwards:\n", "\n", "| Model | Transition | Question |\n", "|---|---|---|\n", "| Constant rate | `tm.Static()` | homogeneous Poisson process (null) |\n", "| Gradually varying | `tm.GaussianRandomWalk` (HyperStudy over step size σ) | does the rate drift? |\n", "| Regime-switching | `tm.RegimeSwitch` | does the rate jump between levels? |\n", "\n", "Because all three share the identical Poisson observation model and data, their\n", "log₁₀ marginal evidences are directly comparable, and the marginalisation\n", "automatically penalises the extra flexibility of the time-varying models." ] }, { "cell_type": "code", "execution_count": 3, "id": "b99c0f50", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:47.773213Z", "iopub.status.busy": "2026-06-12T09:52:47.773071Z", "iopub.status.idle": "2026-06-12T09:52:48.365104Z", "shell.execute_reply": "2026-06-12T09:52:48.364665Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n", "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Regime-switching model. Hyper-Parameter(s): ['log10p_min']\n", "\n", "MODEL EVIDENCE (log10):\n", " Constant rate (Poisson) -65.092\n", " Gradually varying rate -65.801\n", " Regime-switching rate -65.195\n", " best: Constant rate (Poisson) (Delta vs constant: -0.71 for varying)\n" ] } ], "source": [ "def poisson():\n", " return bl.om.Poisson(\"rate\", bl.oint(0, 5, 600))\n", "\n", "# M1 — Static (constant rate): the homogeneous-Poisson null.\n", "S0 = bl.Study()\n", "S0.load_data(counts, timestamps=years)\n", "S0.set(poisson(), bl.tm.Static())\n", "S0.fit(silent=True)\n", "\n", "# M2 — Gradually varying rate (HyperStudy marginalises the random-walk step size).\n", "S2 = bl.HyperStudy()\n", "S2.load_data(counts, timestamps=years)\n", "S2.set(poisson(), bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 0.5, 40), target=\"rate\"))\n", "S2.fit(silent=True)\n", "sig_x, sig_p = S2.get_hyper_parameter_distribution(\"sigma\")\n", "rate_mean, rate_lo, rate_hi = posterior_band(S2, \"rate\")\n", "\n", "# M3 — Regime-switching rate (allows rare abrupt jumps).\n", "S3 = bl.Study()\n", "S3.load_data(counts, timestamps=years)\n", "S3.set(poisson(), bl.tm.RegimeSwitch(\"log10p_min\", -3))\n", "S3.fit(silent=True)\n", "\n", "evid = {\n", " \"Constant rate (Poisson)\": float(S0.log10_evidence),\n", " \"Gradually varying rate\": float(S2.log10_evidence),\n", " \"Regime-switching rate\": float(S3.log10_evidence),\n", "}\n", "best = max(evid, key=evid.get)\n", "print(\"\\nMODEL EVIDENCE (log10):\")\n", "for k, v in evid.items():\n", " print(f\" {k:26s} {v:9.3f}\")\n", "print(f\" best: {best} (Delta vs constant: \"\n", " f\"{evid['Gradually varying rate']-evid['Constant rate (Poisson)']:+.2f} for varying)\")" ] }, { "cell_type": "markdown", "id": "4ad08e00", "metadata": {}, "source": [ "The evidence favours the constant rate. The static homogeneous-Poisson model wins\n", "outright, and the flexible random-walk model is the worst of the three, paying an Occam\n", "penalty for freedom it does not need (≈ 10⁰·⁷ ≈ 5× less probable). A method that could\n", "only ever find time-variation would be of little use here, whereas *bayesloop*'s\n", "marginal-likelihood model selection returns a principled null result.\n", "\n", "We can make the statement sharper by looking at the posterior of the random-walk\n", "step size σ (the magnitude of year-to-year rate change), and by checking that the\n", "verdict is not an artefact of the prior on the rate." ] }, { "cell_type": "code", "execution_count": 4, "id": "d0d2111b", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:48.366296Z", "iopub.status.busy": "2026-06-12T09:52:48.366213Z", "iopub.status.idle": "2026-06-12T09:52:48.939087Z", "shell.execute_reply": "2026-06-12T09:52:48.938631Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P(sigma <= 0.05) = 0.42; sigma posterior mode = 0.000 (0 -> no temporal variation)\n", "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n", "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Prior sensitivity (static - gradual, log10): Jeffreys +0.71, flat +0.79 (both favour constant)\n" ] } ], "source": [ "# Posterior mass on \"essentially no variation\" (sigma small)\n", "p_sigma_small = float(np.sum(sig_p[sig_x <= 0.05]))\n", "print(f\"P(sigma <= 0.05) = {p_sigma_small:.2f}; sigma posterior mode = \"\n", " f\"{sig_x[np.argmax(sig_p)]:.3f} (0 -> no temporal variation)\")\n", "\n", "# Prior sensitivity: the constant-rate preference should not hinge on the default (Jeffreys)\n", "# prior on the rate. Re-run static vs gradual under a FLAT prior and confirm the verdict holds.\n", "def pois_flat():\n", " return bl.om.Poisson(\"rate\", bl.oint(0, 5, 600), prior=lambda rate: np.ones_like(rate))\n", "S0f = bl.Study(); S0f.load_data(counts, timestamps=years); S0f.set(pois_flat(), bl.tm.Static()); S0f.fit(silent=True)\n", "S2f = bl.HyperStudy(); S2f.load_data(counts, timestamps=years)\n", "S2f.set(pois_flat(), bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 0.5, 40), target=\"rate\")); S2f.fit(silent=True)\n", "print(f\"Prior sensitivity (static - gradual, log10): Jeffreys \"\n", " f\"{S0.log10_evidence - S2.log10_evidence:+.2f}, flat \"\n", " f\"{S0f.log10_evidence - S2f.log10_evidence:+.2f} (both favour constant)\")" ] }, { "cell_type": "markdown", "id": "1669cde9", "metadata": {}, "source": [ "The σ posterior peaks at exactly 0 and decays monotonically, so the data prefer *no*\n", "temporal variation at all, which is the strongest Bayesian statement of constancy one\n", "can make. The constant model stays ahead by essentially the same margin whether we use\n", "the default Jeffreys prior on the rate or a flat one, so the verdict is not an artefact\n", "of the prior.\n", "\n", "## A change-point scan around 2004\n", "\n", "As a final check, we force a single rate change-point and let `ChangepointStudy`\n", "scan every possible year, then ask whether the posterior concentrates near 2004 (the\n", "Sumatra event). A genuine break would show up as a sharp peak." ] }, { "cell_type": "code", "execution_count": 5, "id": "5bffa098", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:48.940291Z", "iopub.status.busy": "2026-06-12T09:52:48.940204Z", "iopub.status.idle": "2026-06-12T09:52:49.303983Z", "shell.execute_reply": "2026-06-12T09:52:49.302962Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", " --> Change-point analysis\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Change-point study: evidence -67.76; posterior mode 1902 carries only 0.07 probability (diffuse -> no compelling single break).\n", "Inferred mean rate 2004-2011 = 1.03/yr vs long-term 0.79/yr\n" ] } ], "source": [ "Scp = bl.ChangepointStudy()\n", "Scp.load_data(counts, timestamps=years)\n", "Scp.set(poisson(), bl.tm.ChangePoint(\"t_change\", \"all\"))\n", "Scp.fit(silent=True)\n", "cp_x, cp_p = Scp.get_hyper_parameter_distribution(\"t_change\")\n", "# how concentrated is the change-point posterior? (a real break -> sharp peak)\n", "cp_mode = float(cp_x[np.argmax(cp_p)])\n", "cp_modeprob = float(cp_p.max())\n", "print(f\"Change-point study: evidence {Scp.log10_evidence:.2f}; \"\n", " f\"posterior mode {cp_mode:.0f} carries only {cp_modeprob:.2f} probability \"\n", " f\"(diffuse -> no compelling single break).\")\n", "\n", "# How elevated was the rate during the 2004-2011 window vs long-term?\n", "idx = (years >= 2004) & (years <= 2011)\n", "print(f\"Inferred mean rate 2004-2011 = {rate_mean[idx].mean():.2f}/yr \"\n", " f\"vs long-term {mean_rate:.2f}/yr\")" ] }, { "cell_type": "markdown", "id": "3b0744e0", "metadata": {}, "source": [ "The change-point posterior is diffuse: its modal year carries only a few percent\n", "of the probability and there is no concentration near 2004. There is no break to\n", "find. Even the most flexible inferred rate barely lifts during 2004–2011 (to ≈ 1/yr,\n", "against a ≈ 0.8/yr long-term mean), and its credible band still comfortably contains the\n", "constant rate. We now visualise these results." ] }, { "cell_type": "markdown", "id": "b896ee85", "metadata": {}, "source": [ "### Figure 1 — inferred rate against the constant\n", "\n", "Annual M ≥ 8 counts (bars), the most flexible inferred rate with its 90% credible band\n", "(red), and the long-term mean (dashed). The two highlighted windows are the 1952–65 and\n", "2004–12 clusters of M ≥ 8.5 giants." ] }, { "cell_type": "code", "execution_count": 6, "id": "45058218", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:49.305095Z", "iopub.status.busy": "2026-06-12T09:52:49.305004Z", "iopub.status.idle": "2026-06-12T09:52:49.471695Z", "shell.execute_reply": "2026-06-12T09:52:49.471345Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 4.0))\n", "ax.bar(years, counts, width=0.85, color=C[\"muted\"], alpha=0.5, label=f\"M$\\\\geq${THR:.0f} count\")\n", "ax.plot(years, rate_mean, color=C[\"accent\"], lw=2.0, label=\"inferred rate (posterior mean)\")\n", "ax.fill_between(years, rate_lo, rate_hi, color=C[\"band\"], alpha=0.20, label=\"90% credible band\")\n", "ax.axhline(mean_rate, color=\"k\", ls=\"--\", lw=1, label=f\"long-term mean {mean_rate:.2f}/yr\")\n", "for x0, x1, lab in [(1950, 1965, \"1952–65\\ngiant cluster\"), (2004, 2012, \"2004–12\\ngiant cluster\")]:\n", " ax.axvspan(x0, x1, color=C[\"accent2\"], alpha=0.08)\n", " ax.text((x0 + x1) / 2, 3.5, lab, ha=\"center\", fontsize=7.5, color=C[\"accent2\"])\n", "ax.set_xlim(1899, 2026)\n", "ax.set_ylim(0, 4.3)\n", "ax.set_ylabel(\"great earthquakes per year\")\n", "ax.set_xlabel(\"year\")\n", "ax.set_title(\"Global M$\\\\geq$8 earthquakes: the time-varying rate stays consistent with a constant\")\n", "ax.legend(loc=\"upper left\", ncol=2, fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3955c8fb", "metadata": {}, "source": [ "### Figure 2 — model evidence and the σ posterior\n", "\n", "Left: log₁₀ evidence of each model relative to the worst. Right: the posterior for the\n", "random-walk step size σ, which concentrates at zero." ] }, { "cell_type": "code", "execution_count": 7, "id": "815906a6", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:49.472930Z", "iopub.status.busy": "2026-06-12T09:52:49.472831Z", "iopub.status.idle": "2026-06-12T09:52:49.608158Z", "shell.execute_reply": "2026-06-12T09:52:49.607794Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (axa, axb) = plt.subplots(1, 2, figsize=(9.2, 3.4))\n", "labels = list(evid.keys())\n", "vals = np.array([evid[k] for k in labels]) - min(evid.values())\n", "axa.barh(labels, vals, color=[C[\"accent2\"], C[\"accent\"], C[\"green\"]])\n", "axa.set_xlabel(r\"log$_{10}$ evidence vs worst\")\n", "axa.set_title(\"Model evidence\")\n", "for i, v in enumerate(vals):\n", " axa.text(v, i, f\" {v:.2f}\", va=\"center\", fontsize=8)\n", "axb.bar(sig_x, sig_p, width=(sig_x[1] - sig_x[0]) * 0.9, color=C[\"accent\"], alpha=0.8)\n", "axb.set_xlabel(\"random-walk step size σ of the rate\")\n", "axb.set_ylabel(\"posterior probability\")\n", "axb.set_title(f\"σ posterior peaks at {sig_x[np.argmax(sig_p)]:.3f}\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "18b53f03", "metadata": {}, "source": [ "### Figure 3 — the change-point posterior\n", "\n", "Forcing a single break anywhere in the century, the posterior spreads across the whole\n", "record with no peak at 2004." ] }, { "cell_type": "code", "execution_count": 8, "id": "057b2035", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:52:49.609523Z", "iopub.status.busy": "2026-06-12T09:52:49.609426Z", "iopub.status.idle": "2026-06-12T09:52:49.697484Z", "shell.execute_reply": "2026-06-12T09:52:49.697131Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 3.2))\n", "ax.bar(cp_x, cp_p, width=0.9, color=C[\"green\"], alpha=0.8)\n", "ax.axvline(2004, color=C[\"accent\"], ls=\"--\", lw=1.2, label=\"2004 (Sumatra)\")\n", "ax.set_xlim(1900, 2025)\n", "ax.set_xlabel(\"hypothesised year of a rate change-point\")\n", "ax.set_ylabel(\"posterior probability\")\n", "ax.set_title(\"No compelling break: change-point posterior is diffuse across the century\")\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "11cf7d4a", "metadata": {}, "source": [ "## What this example showed\n", "\n", "*bayesloop* independently reproduces the main result of Shearer & Stark (2012): the\n", "global rate of great earthquakes shows no statistically significant increase. On this\n", "single dataset we used the framework to:\n", "\n", "- return a principled null result: with one shared Poisson observation model, the\n", " marginal-likelihood model selection prefers the constant rate over both a gradual\n", " random walk and a regime switch, and the σ posterior concentrates at zero;\n", "- stress-test that verdict: it survives swapping the Jeffreys prior on the rate for\n", " a flat one, and a `ChangepointStudy` scan finds no concentrated break near 2004;\n", "- use a representative toolbox: `Study`, `HyperStudy`, `ChangepointStudy`, a\n", " `Poisson` observation model, and `Static` / `GaussianRandomWalk` / `RegimeSwitch` /\n", " `ChangePoint` transitions, all directly comparable through their evidence.\n", "\n", "It pairs naturally with the measles example: the *same* Poisson machinery leads to the\n", "*opposite* conclusion, illustrating that the evidence, not the modeller,\n", "decides. The 2004–2011 cluster of giant earthquakes (and the comparably sized 1952–1965\n", "one before it) is what a constant ≈ 0.8/year Poisson process produces from time\n", "to time." ] } ], "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 }